diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 79fded4..4f27edf 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -5,13 +5,13 @@
 Unoptimized:
 
 ```sh
-cargo build
+cargo build --release -p netsaur
 ```
 
 Optimized:
 
 ```sh
-cargo build --release
+deno run build:cpu
 ```
 
 ## Building `backends/wasm`
@@ -19,11 +19,33 @@ cargo build --release
 Unoptimized:
 
 ```sh
-deno run -A https://deno.land/x/wasmbuild@0.11.0/main.ts --out src/backends/wasm/lib --debug
+deno -Ar jsr:@deno/wasmbuild@0.17.2 -p netsaur --out src/backends/wasm/lib --debug
 ```
 
 Optimized:
 
 ```sh
-deno run -A https://deno.land/x/wasmbuild@0.11.0/main.ts --out src/backends/wasm/lib
+deno run build:wasm
+```
+
+## Building `tokenizers`
+
+Unoptimized:
+
+```sh
+deno -Ar jsr:@deno/wasmbuild@0.17.2 -p netsaur-tokenizers --out tokenizers/lib --debug
+```
+
+Optimized:
+
+```sh
+deno run build:tokenizers
+```
+
+## Building everything
+
+Optimized:
+
+```sh
+deno run build
 ```
diff --git a/Cargo.lock b/Cargo.lock
index 042786f..3abdc93 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -91,9 +91,9 @@ checksum = "dc4cab390f4a32340211f015292a4551742a63e528e9ade9e0bde0d1a989d2a1"
 
 [[package]]
 name = "darling"
-version = "0.14.4"
+version = "0.20.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7b750cb3417fd1b327431a470f388520309479ab0bf5e323505daf0290cd3850"
+checksum = "6f63b86c8a8826a49b8c21f08a2d07338eec8d900540f8630dc76284be802989"
 dependencies = [
  "darling_core",
  "darling_macro",
@@ -101,58 +101,58 @@ dependencies = [
 
 [[package]]
 name = "darling_core"
-version = "0.14.4"
+version = "0.20.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "109c1ca6e6b7f82cc233a97004ea8ed7ca123a9af07a8230878fcfda9b158bf0"
+checksum = "95133861a8032aaea082871032f5815eb9e98cef03fa916ab4500513994df9e5"
 dependencies = [
  "fnv",
  "ident_case",
  "proc-macro2",
  "quote",
  "strsim",
- "syn 1.0.109",
+ "syn",
 ]
 
 [[package]]
 name = "darling_macro"
-version = "0.14.4"
+version = "0.20.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a4aab4dbc9f7611d8b55048a3a16d2d010c2c8334e46304b40ac1cc14bf3b48e"
+checksum = "d336a2a514f6ccccaa3e09b02d41d35330c07ddf03a62165fcec10bb561c7806"
 dependencies = [
  "darling_core",
  "quote",
- "syn 1.0.109",
+ "syn",
 ]
 
 [[package]]
 name = "derive_builder"
-version = "0.12.0"
+version = "0.20.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8d67778784b508018359cbc8696edb3db78160bab2c2a28ba7f56ef6932997f8"
+checksum = "cd33f37ee6a119146a1781d3356a7c26028f83d779b2e04ecd45fdc75c76877b"
 dependencies = [
  "derive_builder_macro",
 ]
 
 [[package]]
 name = "derive_builder_core"
-version = "0.12.0"
+version = "0.20.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c11bdc11a0c47bc7d37d582b5285da6849c96681023680b906673c5707af7b0f"
+checksum = "7431fa049613920234f22c47fdc33e6cf3ee83067091ea4277a3f8c4587aae38"
 dependencies = [
  "darling",
  "proc-macro2",
  "quote",
- "syn 1.0.109",
+ "syn",
 ]
 
 [[package]]
 name = "derive_builder_macro"
-version = "0.12.0"
+version = "0.20.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ebcda35c7a396850a55ffeac740804b40ffec779b98fffbb1738f4033f0ee79e"
+checksum = "4abae7035bf79b9877b779505d8cf3749285b80c43941eda66604841889451dc"
 dependencies = [
  "derive_builder_core",
- "syn 1.0.109",
+ "syn",
 ]
 
 [[package]]
@@ -169,12 +169,13 @@ checksum = "d817e038c30374a4bcb22f94d0a8a0e216958d4c3dcde369b1439fec4bdda6e6"
 
 [[package]]
 name = "fancy-regex"
-version = "0.11.0"
+version = "0.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b95f7c0680e4142284cf8b22c14a476e87d61b004a3a0861872b32ef7ead40a2"
+checksum = "531e46835a22af56d1e3b66f04844bed63158bc094a628bec1d321d9b4c44bf2"
 dependencies = [
  "bit-set",
- "regex",
+ "regex-automata",
+ "regex-syntax",
 ]
 
 [[package]]
@@ -211,6 +212,15 @@ dependencies = [
  "either",
 ]
 
+[[package]]
+name = "itertools"
+version = "0.12.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ba291022dbbd398a455acf126c1e341954079855bc60dfdda641363bd6922569"
+dependencies = [
+ "either",
+]
+
 [[package]]
 name = "itoa"
 version = "1.0.9"
@@ -278,9 +288,9 @@ dependencies = [
 
 [[package]]
 name = "memchr"
-version = "2.5.0"
+version = "2.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2dffe52ecf27772e601905b7522cb4ef790d2cc203488bbd0e2fe85fcb74566d"
+checksum = "78ca9ab1a0babb1e7d5695e3530886289c18cf2f87ec19a575a0abdce112e3a3"
 
 [[package]]
 name = "memoffset"
@@ -299,9 +309,9 @@ checksum = "68354c5c6bd36d73ff3feceb05efa59b6acb7626617f4962be322a825e61f79a"
 
 [[package]]
 name = "monostate"
-version = "0.1.9"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "15f370ae88093ec6b11a710dec51321a61d420fafd1bad6e30d01bd9c920e8ee"
+checksum = "0d208407d7552cd041d8cdb69a1bc3303e029c598738177a3d87082004dc0e1e"
 dependencies = [
  "monostate-impl",
  "serde",
@@ -309,13 +319,13 @@ dependencies = [
 
 [[package]]
 name = "monostate-impl"
-version = "0.1.9"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "371717c0a5543d6a800cac822eac735aa7d2d2fbb41002e9856a4089532dbdce"
+checksum = "a7ce64b975ed4f123575d11afd9491f2e37bbd5813fbfbc0f09ae1fbddea74e0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.28",
+ "syn",
 ]
 
 [[package]]
@@ -344,7 +354,7 @@ dependencies = [
 
 [[package]]
 name = "netsaur"
-version = "0.3.1-patch"
+version = "0.4.0"
 dependencies = [
  "getrandom",
  "js-sys",
@@ -358,7 +368,7 @@ dependencies = [
 
 [[package]]
 name = "netsaur-gpu"
-version = "0.3.1-patch"
+version = "0.4.0"
 dependencies = [
  "cudarc",
  "ndarray",
@@ -371,7 +381,7 @@ dependencies = [
 
 [[package]]
 name = "netsaur-tokenizers"
-version = "0.3.1-patch"
+version = "0.4.0"
 dependencies = [
  "getrandom",
  "js-sys",
@@ -443,18 +453,18 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de"
 
 [[package]]
 name = "proc-macro2"
-version = "1.0.66"
+version = "1.0.86"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "18fb31db3f9bddb2ea821cde30a9f70117e3f119938b5ee630b7403aa6e2ead9"
+checksum = "5e719e8df665df0d1c8fbfd238015744736151d4445ec0836b8e628aae103b77"
 dependencies = [
  "unicode-ident",
 ]
 
 [[package]]
 name = "quote"
-version = "1.0.32"
+version = "1.0.37"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "50f3b39ccfb720540debaa0164757101c08ecb8d326b15358ce76a62c7e85965"
+checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
 dependencies = [
  "proc-macro2",
 ]
@@ -507,9 +517,9 @@ checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3"
 
 [[package]]
 name = "rayon"
-version = "1.8.0"
+version = "1.10.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9c27db03db7734835b3f53954b534c91069375ce6ccaa2e065441e07d9b6cdb1"
+checksum = "b418a60154510ca1a002a752ca9714984e21e4241e804d32555251faf8b78ffa"
 dependencies = [
  "either",
  "rayon-core",
@@ -522,15 +532,15 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "059f538b55efd2309c9794130bc149c6a553db90e9d99c2030785c82f0bd7df9"
 dependencies = [
  "either",
- "itertools",
+ "itertools 0.11.0",
  "rayon",
 ]
 
 [[package]]
 name = "rayon-core"
-version = "1.12.0"
+version = "1.12.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5ce3fb6ad83f861aac485e76e1985cd109d9a3713802152be56c3b1f0e0658ed"
+checksum = "1465873a3dfdaa8ae7cb14b4383657caab0b3e8a0aa9ae8e04b044854c8dfce2"
 dependencies = [
  "crossbeam-deque",
  "crossbeam-utils",
@@ -538,9 +548,9 @@ dependencies = [
 
 [[package]]
 name = "regex"
-version = "1.9.3"
+version = "1.10.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "81bc1d4caf89fac26a70747fe603c130093b53c773888797a6329091246d651a"
+checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -550,9 +560,9 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.3.6"
+version = "0.4.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fed1ceff11a1dddaee50c9dc8e4938bd106e9d89ae372f192311e7da498e3b69"
+checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -561,9 +571,9 @@ dependencies = [
 
 [[package]]
 name = "regex-syntax"
-version = "0.7.4"
+version = "0.8.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e5ea92a5b6195c6ef2a0295ea818b312502c6fc94dde986c5553242e18fd4ce2"
+checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
 
 [[package]]
 name = "ryu"
@@ -615,7 +625,7 @@ checksum = "be02f6cb0cd3a5ec20bbcfbcbd749f57daddb1a0882dc2e46a6c236c90b977ed"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.28",
+ "syn",
 ]
 
 [[package]]
@@ -649,26 +659,15 @@ dependencies = [
 
 [[package]]
 name = "strsim"
-version = "0.10.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "73473c0e59e6d5812c5dfe2a064a6444949f089e20eec9a2e5506596494e4623"
-
-[[package]]
-name = "syn"
-version = "1.0.109"
+version = "0.11.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "72b64191b275b66ffe2469e8af2c1cfe3bafa67b529ead792a6d0160888b4237"
-dependencies = [
- "proc-macro2",
- "quote",
- "unicode-ident",
-]
+checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
 
 [[package]]
 name = "syn"
-version = "2.0.28"
+version = "2.0.77"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "04361975b3f5e348b2189d8dc55bc942f278b2d482a6a0365de5bdd62d351567"
+checksum = "9f35bcdf61fd8e7be6caf75f429fdca8beb3ed76584befb503b1569faee373ed"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -692,21 +691,21 @@ checksum = "10712f02019e9288794769fba95cd6847df9874d49d871d062172f9dd41bc4cc"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.28",
+ "syn",
 ]
 
 [[package]]
 name = "tokenizers"
-version = "0.14.1"
+version = "0.20.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d9be88c795d8b9f9c4002b3a8f26a6d0876103a6f523b32ea3bac52d8560c17c"
+checksum = "c8a24d7f7d6be5b9d1377418b893ab1808af0074f5d1bb2c64784452ddd2aa70"
 dependencies = [
  "aho-corasick",
  "derive_builder",
  "esaxx-rs",
  "fancy-regex",
  "getrandom",
- "itertools",
+ "itertools 0.12.1",
  "lazy_static",
  "log",
  "macro_rules_attribute",
@@ -743,9 +742,9 @@ dependencies = [
 
 [[package]]
 name = "unicode-segmentation"
-version = "1.10.1"
+version = "1.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1dd624098567895118886609431a7c3b8f516e41d30e0643f03d94592a147e36"
+checksum = "d4c87d22b6e3f4a18d4d40ef354e97c90fcb14dd91d7dc0aa9d8a1172ebf7202"
 
 [[package]]
 name = "unicode_categories"
@@ -780,7 +779,7 @@ dependencies = [
  "once_cell",
  "proc-macro2",
  "quote",
- "syn 2.0.28",
+ "syn",
  "wasm-bindgen-shared",
 ]
 
@@ -802,7 +801,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.28",
+ "syn",
  "wasm-bindgen-backend",
  "wasm-bindgen-shared",
 ]
diff --git a/Cargo.toml b/Cargo.toml
index c1f3264..657fd35 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -1,5 +1,4 @@
 [workspace]
-
-members = [
-    "crates/*"
-]
\ No newline at end of file
+package.version = "0.4.0"
+members = ["crates/*"]
+resolver = "2"
diff --git a/README.md b/README.md
index 5be11fe..17884d4 100644
--- a/README.md
+++ b/README.md
@@ -19,10 +19,15 @@
 
 ## Powerful Machine Learning library for Deno
 
+## Installation
+
+There is no installation step required. You can simply import the library and
+you're good to go :)
+
 ## Features
 
 - Lightweight and easy-to-use neural network library for
-  [Deno](https://deno.land).
+  [Deno](https://deno.com).
 - Blazingly fast and efficient.
 - Provides a simple API for creating and training neural networks.
 - Can run on both the CPU and the GPU (WIP).
@@ -51,6 +56,7 @@
 
 - Dean Srebnik ([@load1n9](https://github.com/load1n9))
 - CarrotzRule ([@carrotzrule123](https://github.com/CarrotzRule123))
+- Pranev ([@retraigo](https://github.com/retraigo))
 
 ### QuickStart
 
@@ -66,9 +72,8 @@ import {
   Sequential,
   setupBackend,
   SigmoidLayer,
-  tensor1D,
   tensor2D,
-} from "https://deno.land/x/netsaur/mod.ts";
+} from "jsr:@denosaurs/netsaur";
 
 /**
  * Setup the CPU backend. This backend is fast but doesn't work on the Edge.
@@ -111,8 +116,6 @@ const net = new Sequential({
   cost: Cost.MSE,
 });
 
-const time = performance.now();
-
 /**
  * Train the network on the given data.
  */
@@ -134,8 +137,6 @@ net.train(
   10000,
 );
 
-console.log(`training time: ${performance.now() - time}ms`);
-
 /**
  * Predict the output of the XOR function for the given inputs.
  */
@@ -167,7 +168,7 @@ import {
   tensor1D,
   tensor2D,
   WASM,
-} from "https://deno.land/x/netsaur/mod.ts";
+} from "jsr:@denosaurs/netsaur";
 
 /**
  * Setup the WASM backend. This backend is slower than the CPU backend but works on the Edge.
@@ -210,8 +211,6 @@ const net = new Sequential({
   cost: Cost.MSE,
 });
 
-const time = performance.now();
-
 /**
  * Train the network on the given data.
  */
@@ -233,8 +232,6 @@ net.train(
   10000,
 );
 
-console.log(`training time: ${performance.now() - time}ms`);
-
 /**
  * Predict the output of the XOR function for the given inputs.
  */
@@ -254,7 +251,7 @@ console.log(`1 xor 1 = ${out4[0]} (should be close to 0)`);
 ### Documentation
 
 The full documentation for Netsaur can be found
-[here](https://deno.land/x/netsaur@0.3.1-patch/mod.ts).
+[here](https://deno.land/x/netsaur/mod.ts).
 
 ### License
 
diff --git a/crates/core-gpu/Cargo.toml b/crates/core-gpu/Cargo.toml
index db058c3..028a8c3 100644
--- a/crates/core-gpu/Cargo.toml
+++ b/crates/core-gpu/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 edition = "2021"
 name = "netsaur-gpu"
-version = "0.3.1-patch"
+version = { workspace = true }
 
 [lib]
 crate-type = ["cdylib"]
diff --git a/crates/core/Cargo.toml b/crates/core/Cargo.toml
index 9da1efe..e5f7cc1 100644
--- a/crates/core/Cargo.toml
+++ b/crates/core/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 edition = "2021"
 name = "netsaur"
-version = "0.3.1-patch"
+version = { workspace = true }
 
 [lib]
 crate-type = ["cdylib"]
@@ -9,7 +9,7 @@ crate-type = ["cdylib"]
 [dependencies]
 ndarray = "0.15.6"
 ndarray-rand = "0.14.0"
-serde = {version = "1.0", features = ["derive"]}
+serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
 safetensors = "0.4.0"
 
diff --git a/crates/tokenizers/Cargo.toml b/crates/tokenizers/Cargo.toml
index 1f8a351..2538ca6 100644
--- a/crates/tokenizers/Cargo.toml
+++ b/crates/tokenizers/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 edition = "2021"
 name = "netsaur-tokenizers"
-version = "0.3.1-patch"
+version = { workspace = true }
 
 [lib]
 crate-type = ["cdylib"]
@@ -12,7 +12,7 @@ ndarray-rand = "0.14.0"
 serde = {version = "1.0", features = ["derive"]}
 serde_json = "1.0"
 serde-wasm-bindgen = "0.6.0"
-tokenizers = { version="0.14.1", default-features=false, features = ["unstable_wasm"]}
+tokenizers = { version="0.20.0", default-features=false, features = ["unstable_wasm"]}
 wasm-bindgen = "0.2.92"
 getrandom = { version = "0.2", features = ["js"] }
 js-sys = "0.3.69"
diff --git a/data/deps.ts b/data/deps.ts
deleted file mode 100644
index b236b66..0000000
--- a/data/deps.ts
+++ /dev/null
@@ -1 +0,0 @@
-export { CsvParseStream } from "jsr:@std/csv@0.214.0";
diff --git a/deno.json b/deno.json
deleted file mode 100644
index 69f7422..0000000
--- a/deno.json
+++ /dev/null
@@ -1,36 +0,0 @@
-{
-  "name": "@denosaurs/netsaur",
-  "version": "0.3.2-patch3",
-  "exports": {
-    ".": "./mod.ts",
-    "./web": "./web.ts",
-    "./tokenizers": "./tokenizers/mod.ts",
-    "./visualizer": "./visualizer/mod.ts",
-    "./data": "./data/mod.ts"
-  },
-  "tasks": {
-    "example:xor": "deno run -A --unstable-ffi ./examples/xor_auto.ts",
-    "example:xor-option": "deno run -A --unstable-ffi ./examples/xor_option.ts",
-    "example:xor-cpu": "deno run -A --unstable-ffi ./examples/xor_cpu.ts",
-    "example:xor-gpu": "deno run -A --unstable-ffi ./examples/xor_gpu.ts",
-    "example:xor-wasm": "deno run -A ./examples/xor_wasm.ts",
-    "example:autoencoder": "deno run -A --unstable-ffi ./examples/autoencoders/example.ts",
-    "example:linear": "deno run -A --unstable-ffi ./examples/linear.ts",
-    "example:multiple-linear": "deno run -A --unstable-ffi ./examples/multiple-linear/student.ts",
-    "example:binary": "deno run -A --unstable-ffi ./examples/classification/binary_iris.ts",
-    "example:multiclass": "deno run -A --unstable-ffi ./examples/classification/iris.ts",
-    "example:text": "deno run -A --unstable-ffi ./examples/classification/spam.ts",
-    "example:filters": "deno run -A --unstable-ffi examples/filters/conv.ts ",
-    "example:train": "deno run -A --unstable-ffi examples/model/train.ts ",
-    "example:run": "deno run -A --unstable-ffi examples/model/run.ts ",
-    "example:mnist-download": "deno run -A --unstable-ffi examples/mnist/download.ts ",
-    "example:mnist-train": "deno run -A --unstable-ffi examples/mnist/train.ts ",
-    "example:mnist-predict": "deno run -A --unstable-ffi examples/mnist/predict.ts ",
-    "example:tokenizers-basic": "deno run -A examples/tokenizers/basic.ts",
-    "build": "deno task build:cpu && deno task build:wasm && deno task build:tokenizers",
-    "build:cpu": "cargo build --release -p netsaur",
-    "build:gpu": "cargo build --release -p netsaur-gpu",
-    "build:wasm": "deno run -Ar jsr:@deno/wasmbuild@0.17.1 -p netsaur --out src/backends/wasm/lib",
-    "build:tokenizers": "deno run -Ar jsr:@deno/wasmbuild@0.17.1 -p netsaur-tokenizers --out tokenizers/lib"
-  }
-}
diff --git a/deno.jsonc b/deno.jsonc
new file mode 100644
index 0000000..76e22be
--- /dev/null
+++ b/deno.jsonc
@@ -0,0 +1,56 @@
+{
+  "name": "@denosaurs/netsaur",
+  "version": "0.3.2-patch3",
+  "exports": {
+    ".": "./mod.ts",
+    "./web": "./web.ts",
+    // Data
+    "./data": "./packages/data/mod.ts",
+    // Core
+    "./core/engine": "./packages/core/src/core/engine.ts",
+    "./core": "./packages/core/src/core/mod.ts",
+    "./core/types": "./packages/core/src/core/types.ts",
+    "./core/tensor": "./packages/core/src/core/tensor/tensor.ts",
+    "./core/layers": "./packages/core/src/core/api/layers.ts",
+    "./core/shape": "./packages/core/src/core/api/shape.ts",
+    "./core/models": "./packages/core/src/core/api/network.ts",
+    "./core/optimizers": "./packages/core/src/core/api/optimizer.ts",
+    "./core/schedulers": "./packages/core/src/core/api/scheduler.ts",
+    // Utilities
+    "./utilities": "./packages/utilities/src/mod.ts",
+    "./utilities/text": "./packages/utilities/src/text/mod.ts",
+    "./utilities/image": "./packages/utilities/src/image/mod.ts",
+    "./utilities/metrics": "./packages/utilities/src/metrics/mod.ts",
+    "./utilities/encoding": "./packages/utilities/src/encoding/mod.ts",
+    "./utilities/misc": "./packages/utilities/src/utils/mod.ts",
+    // Tokenizers
+    "./tokenizers": "./packages/tokenizers/mod.ts",
+    // Visualizer
+    "./visualizer": "./packages/visualizer/mod.ts"
+  },
+  "tasks": {
+    "example:xor": "deno -A ./examples/xor_auto.ts",
+    "example:xor-option": "deno -A ./examples/xor_option.ts",
+    "example:xor-cpu": "deno -A ./examples/xor_cpu.ts",
+    "example:xor-gpu": "deno -A ./examples/xor_gpu.ts",
+    "example:xor-wasm": "deno -A ./examples/xor_wasm.ts",
+    "example:autoencoder": "deno -A ./examples/autoencoders/example.ts",
+    "example:linear": "deno -A ./examples/linear.ts",
+    "example:multiple-linear": "deno -A ./examples/multiple-linear/student.ts",
+    "example:binary": "deno -A ./examples/classification/binary_iris.ts",
+    "example:multiclass": "deno -A ./examples/classification/iris.ts",
+    "example:text": "deno -A ./examples/classification/spam.ts",
+    "example:filters": "deno -A examples/filters/conv.ts ",
+    "example:train": "deno -A examples/model/train.ts ",
+    "example:run": "deno -A examples/model/run.ts ",
+    "example:mnist-download": "deno -A examples/mnist/download.ts",
+    "example:mnist-train": "deno -A examples/mnist/train.ts",
+    "example:mnist-predict": "deno -A examples/mnist/predict.ts",
+    "example:tokenizers-basic": "deno -A examples/tokenizers/basic.ts",
+    "build": "deno run build:cpu && deno run build:wasm && deno run build:tokenizers",
+    "build:cpu": "cargo build --release -p netsaur",
+    "build:gpu": "cargo build --release -p netsaur-gpu",
+    "build:wasm": "deno -Ar jsr:@deno/wasmbuild@0.17.2 -p netsaur --out src/backends/wasm/lib",
+    "build:tokenizers": "deno -Ar jsr:@deno/wasmbuild@0.17.2 -p netsaur-tokenizers --out tokenizers/lib"
+  }
+}
\ No newline at end of file
diff --git a/deno.lock b/deno.lock
index f493492..ce3223b 100644
--- a/deno.lock
+++ b/deno.lock
@@ -1,156 +1,65 @@
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-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/conversion/text/sparse/multi_hot.ts": "48d761f1fd7170655e12c4ea83ea38a5347cfc3268d7b8a9433a757d0ce829f5",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/conversion/text/sparse/tf_idf_transformer.ts": "8e65e0267a2e817579773346581c01f6d5cd211f43cb9d50309dd5d32b88196f",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/conversion/text/types.ts": "b0d11ec71273dec4f47b9eed2ee36253edc141997ac9dca6cdf81d019ad0bd39",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/extraction/image/colors/common.ts": "88d81009371cd53563d6b719644c3d3b11fce18cd111129c127416af534f5dbe",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/extraction/image/colors/histogram.ts": "fc32cf9d374ce80182943fc3af036adf6d486d2b8a02c6aeca83da797d072ac3",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/extraction/image/colors/median_cut.ts": "d299a88041ea010da3f79bd627b8200cb94cfd4492da68d5279a1ce1837358d9",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/extraction/image/colors/mod.ts": "531dc0c7ad315834a9579045b356c7e7842cd9568d5f9ead3a09d0eddac8e2f3",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/extraction/image/patches/patch_2d.ts": "fa0fdf91618c6370acd5a699bee5964c85ee239c1a3cea6b9034da1dacba8972",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/image.ts": "779149bba165f2a135fa29fbfa29733540284553404877fcd7c2941fa9dfd568",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/text.ts": "13ea9d45110a93dac34fad407bee497fea1966dc2c48b60bee80a9a4c333c1a5",
-    "https://deno.land/x/vectorizer@v0.3.7/src/feature/types.ts": "4da6c4de0d573827f16e02a27419163916126bb0a4f50c81e5633b31ffe7b5e4",
-    "https://deno.land/x/vectorizer@v0.3.7/src/metrics/classification.ts": "1745df3247d477ccee61b9cffdde3cea39b5692a0408e576dc435eb4ab831a86",
-    "https://deno.land/x/vectorizer@v0.3.7/src/metrics/mod.ts": "0c851b3522c9ace99f4dc37209c8aa66ece538f40089d45a09cb94c9cd2300ec",
-    "https://deno.land/x/vectorizer@v0.3.7/src/metrics/regression.ts": "803164aac3346268afb76cb2cafa12932350afbbe56475d339273fd77fed6fec",
-    "https://deno.land/x/vectorizer@v0.3.7/src/mod.ts": "10b549cb251c641c0495309e01b80c795dd20a4cb1c59531ea162d73994264b0",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/array/mod.ts": "a95df5671fcb09123d6e8a9aaa46ff2f935ad43966f3dc4d32051da31577ed0f",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/array/range.ts": "a72d2355492889e739a1987b5a3a6c859ac71f2601eb67138b58e6983a24d9d1",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/array/split.ts": "2d57882320b7fe7257845c3921c65470fb6d2691bbf999c9a78325744ecefd6b",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/array/unique.ts": "d81d591c077ad81123ecf612445bcfc657a409b0e10a1667c184c675d7ee895a",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/common_types.ts": "1d02a8e0a9fc3f3a12ccc4072413630bf7d25a7651f01aecd067f5afc97bfdb0",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/datetime/duration.ts": "ad1704be1a47b75d581e8030a677b881c92d8f4a23348669066c01b1e478ed19",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/datetime/mod.ts": "30f93d011754ff647495d38fa38eb9790ae1c55258df9ba5ee615b96dfd30958",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/datetime/time.ts": "e67edba8d60c682ea87fd6457a6460fbbc92a61e78afa41f0536bab8c26b38ac",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/misc/get_constructor.ts": "8588ace02567b46669de92de172e8958124a05050d5ce080b066b81d8fd87a08",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/misc/image.ts": "acfd832d82ff8983abef350277f244c0f893dd2d2b7bc560893b5230f8383a2b",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/misc/matrix.ts": "d1294cbc93f49ba6b009c953003d353684c26df1d18cf899d44ce82a8486e922",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/misc/mod.ts": "f4641debc43d0777e527fa4f289c38574fb10c37ac0e2cf00b50f76860bde896",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/mod.ts": "adcbb62e22a87ca518971d78783b7c5b056085f1f78d01d24fbaaa25bb9b6039",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/random/mod.ts": "8b1d03fd04bad86ece5c1b8b2efa20f56231d9a8bac962cf10571d321d989116",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/random/normal.ts": "c6f0bac64e0e147f8df6f9882eeb7c0bc335bd7a00e06897c6147118333af497",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/random/rearrange.ts": "1c1571664845896663b563029f3f2572fccd38c6d92ec3fd11dc6d7ea866cb4b",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/random/rng.ts": "213dff1b1a39ae4621d35330eb19a3f96170949e8ca429e8028a1fdf86c61f22",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/random/shuffle.ts": "66f966851d5b6dd4c81117d32311742c319ea3042aad22a7874d2b10da4e743f",
-    "https://deno.land/x/vectorizer@v0.3.7/src/utils/random/weighted.ts": "c4eeb309477ecf431a0b9fd1bf0f04540825194c7ed30ba8c71c9e45e14112e6",
-    "https://deno.land/x/wasmbuild@0.15.6/loader/cache.ts": "362d03a2ceb3d3e130373102a5de6d54ffda659a0911c2fcc090c29c3555f7ef",
-    "https://deno.land/x/wasmbuild@0.15.6/loader/fetch.ts": "7d015203a93cbadd05034cbb6e726d35f47e1f51140d91e69aeddc5d71b06ec8"
+    "https://deno.land/std@0.106.0/encoding/base64.ts": "eecae390f1f1d1cae6f6c6d732ede5276bf4b9cd29b1d281678c054dc5cc009e",
+    "https://deno.land/x/canvas@v1.4.1/deps.ts": "e956026d98094946166e06d7b799290b732db015813870d84e04e33ab88e98f3",
+    "https://deno.land/x/canvas@v1.4.1/mod.ts": "a4e16972647ceafef58612a377a218372454c99d2c9da615a132694597114f80",
+    "https://deno.land/x/canvas@v1.4.1/src/base64.ts": "0928031fdba0c43b617154fbe2eb7578366460c04da1422933ae5e936d3d0349",
+    "https://deno.land/x/canvas@v1.4.1/src/canvas.ts": "58119999b04f68ebeed2627485c5c24c5b0c029707edde0b6568814f9049a3a8",
+    "https://deno.land/x/canvas@v1.4.1/src/canvaskit.ts": "c3d807472cbb3e1d9fc01bb43ff974ef796c4b010178d1595be5fa793cce5e7d",
+    "https://deno.land/x/canvas@v1.4.1/src/color_util.ts": "28f1072f0a5acbe7add7fac2f452311a47b44c080806fc4057de2d2e405c6c1c",
+    "https://deno.land/x/canvas@v1.4.1/src/lib.js": "bb21711589bfbc8997b455cdf53e3150e23289f3b44809188041b1d2fc7924fa",
+    "https://deno.land/x/canvas@v1.4.1/src/types.ts": "67d5800f8f4b0a407e0251676a03ae91b5f50a3ed53e6b72dc5984113cb93128",
+    "https://deno.land/x/canvas@v1.4.1/src/wasm.js": "449d72cc14fc4142a5853f944df49a744d852981d09c5515528ede8aebb0afda",
+    "https://deno.land/x/lz4@v0.1.2/mod.ts": "4decfc1a3569d03fd1813bd39128b71c8f082850fe98ecfdde20025772916582",
+    "https://deno.land/x/lz4@v0.1.2/wasm.js": "b9c65605327ba273f0c76a6dc596ec534d4cda0f0225d7a94ebc606782319e46",
+    "https://deno.land/x/pngs@0.1.1/mod.ts": "9dc8a7daed1497b94a77b68c954164a9f0b2a6f40866481bdfdbbaf015b5f764",
+    "https://deno.land/x/pngs@0.1.1/wasm.js": "e3d4a8f293b267c9859a2164ca7b4603869bc92fe0d5ad4f109925858bce0c4c"
   }
 }
diff --git a/examples/autoencoders/example.ts b/examples/autoencoders/example.ts
index ea8ddb0..352551f 100644
--- a/examples/autoencoders/example.ts
+++ b/examples/autoencoders/example.ts
@@ -1,24 +1,25 @@
-import { Matrix } from "https://deno.land/x/vectorizer@v0.3.6/mod.ts";
+import { Matrix } from "../../packages/utilities/mod.ts";
 import {
-  Sequential,
-  setupBackend,
+  AdamOptimizer,
+  Cost,
   CPU,
   DenseLayer,
-  AdamOptimizer,
-  Shape2D,
   ReluLayer,
+  Sequential,
+  setupBackend,
+  type Shape2D,
   tensor,
-  Cost,
-  OneCycle
-} from "../../mod.ts";;
+} from "../../packages/core/mod.ts";
 
-import { parse } from "https://deno.land/std@0.188.0/csv/parse.ts";
+import { parse } from "jsr:@std/csv@1.0.3/parse";
 
-const data = parse(Deno.readTextFileSync("examples/autoencoders/winequality-red.csv"))
-data.shift()
+const data = parse(
+  Deno.readTextFileSync("examples/autoencoders/winequality-red.csv"),
+);
+data.shift();
 
-const x_data = data.slice(0, 20).map((fl, i) => fl.slice(0, 11).map(Number));
-const X = new Matrix<"f32">(Float32Array.from(x_data.flat()), [x_data.length])
+const x_data = data.slice(0, 20).map((fl) => fl.slice(0, 11).map(Number));
+const X = new Matrix(x_data, "f32");
 
 await setupBackend(CPU);
 
@@ -31,7 +32,7 @@ const net = new Sequential({
     ReluLayer(),
     DenseLayer({ size: [4] }),
     ReluLayer(),
-    DenseLayer({size: [2]}),
+    DenseLayer({ size: [2] }),
     // Decoder
     DenseLayer({ size: [4] }),
     ReluLayer(),
@@ -42,38 +43,41 @@ const net = new Sequential({
   cost: Cost.MSE,
   patience: 50,
   optimizer: AdamOptimizer(),
-//  scheduler: OneCycle()
+  //  scheduler: OneCycle()
 });
 
-const input = tensor(X.data, X.shape)
+const input = tensor(X);
 
-const timeStart = performance.now()
-net.train([{inputs: input, outputs: input}], 10000, 1, 0.01)
-console.log(`Trained in ${performance.now() - timeStart}ms`)
+const timeStart = performance.now();
+net.train([{ inputs: input, outputs: input }], 10000, 1, 0.01);
+console.log(`Trained in ${performance.now() - timeStart}ms`);
 
 function saveTable(name: string, data: Matrix<"f32">) {
-    Deno.writeTextFileSync(`examples/autoencoders/${name}.html`, data.html)
+  Deno.writeTextFileSync(`examples/autoencoders/${name}.html`, data.html);
 }
 
-saveTable("input", X)
+saveTable("input", X);
 
-console.log("Running Whole Net")
-const output = await net.predict(input)
+console.log("Running Whole Net");
+const output = await net.predict(input);
 
-const output_mat = new Matrix<"f32">(output.data, output.shape as Shape2D)
+const output_mat = new Matrix<"f32">(output.data, output.shape as Shape2D);
 
-saveTable("output", output_mat)
+saveTable("output", output_mat);
 
-console.log("Running Encoder")
-const encoded = await net.predict(input, [0, 5])
+console.log("Running Encoder");
+const encoded = await net.predict(input, [0, 5]);
 
-const encoded_mat = new Matrix<"f32">(encoded.data, encoded.shape as Shape2D)
+const encoded_mat = new Matrix<"f32">(encoded.data, encoded.shape as Shape2D);
 
-saveTable("encoded", encoded_mat)
+saveTable("encoded", encoded_mat);
 
-console.log("Running Decoder")
-const decoded = await net.predict(tensor(encoded_mat.data, encoded_mat.shape), [5, 10])
+console.log("Running Decoder");
+const decoded = await net.predict(tensor(encoded_mat), [
+  5,
+  10,
+]);
 
-const decoded_mat = new Matrix<"f32">(decoded.data, decoded.shape as Shape2D)
+const decoded_mat = new Matrix<"f32">(decoded.data, decoded.shape as Shape2D);
 
-saveTable("decoded", decoded_mat)
\ No newline at end of file
+saveTable("decoded", decoded_mat);
diff --git a/examples/classification/binary_iris.ts b/examples/classification/binary_iris.ts
index 01d647b..7378241 100644
--- a/examples/classification/binary_iris.ts
+++ b/examples/classification/binary_iris.ts
@@ -6,16 +6,16 @@ import {
   setupBackend,
   SigmoidLayer,
   tensor2D,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
-import { parse } from "https://deno.land/std@0.204.0/csv/parse.ts";
+import { parse } from "jsr:@std/csv@1.0.3/parse";
 
 // Import helpers for metrics
 import {
   ClassificationReport,
   // Split the dataset
   useSplit,
-} from "https://deno.land/x/vectorizer@v0.3.7/mod.ts";
+} from "../../packages/utilities/mod.ts";
 
 // Define classes
 const classes = ["Setosa", "Versicolor"];
@@ -29,10 +29,7 @@ const x = data.map((fl) => fl.slice(0, 4).map(Number));
 const y = data.map((fl) => classes.indexOf(fl[4]));
 
 // Split the dataset for training and testing
-const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
-  [typeof x, typeof y],
-  [typeof x, typeof y],
-];
+const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y)
 
 // Setup the CPU backend for Netsaur
 await setupBackend(CPU);
diff --git a/examples/classification/iris.ts b/examples/classification/iris.ts
index a2e0036..725a314 100644
--- a/examples/classification/iris.ts
+++ b/examples/classification/iris.ts
@@ -1,5 +1,4 @@
 import {
-  AdamOptimizer,
   Cost,
   CPU,
   DenseLayer,
@@ -9,22 +8,21 @@ import {
   Sequential,
   setupBackend,
   SoftmaxLayer,
-  Tensor,
+  type Tensor,
   tensor,
   tensor2D,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
-import { parse } from "https://deno.land/std@0.204.0/csv/parse.ts";
+import { parse } from "jsr:@std/csv@1.0.3/parse";
 
 // Import helpers for metrics
 import {
   // One-hot encoding of targets
   CategoricalEncoder,
   ClassificationReport,
-  Matrix,
   // Split the dataset
   useSplit,
-} from "jsr:@lala/appraisal@0.7.3";
+} from "../../packages/utilities/mod.ts";
 
 // Read the training dataset
 const _data = Deno.readTextFileSync("examples/classification/iris.csv");
@@ -40,10 +38,7 @@ const y = encoder.fit(y_pre).transform(y_pre, "f32");
 
 // Split the dataset for training and testing
 // @ts-ignore Matrices can be split
-const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
-  [typeof x, typeof y],
-  [typeof x, typeof y]
-];
+const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y)
 
 // Setup the CPU backend for Netsaur
 await setupBackend(CPU);
@@ -89,7 +84,7 @@ net.train(
   // Train for 300 epochs
   400,
   1,
-  0.02
+  0.02,
 );
 
 console.log(`training time: ${performance.now() - time}ms`);
@@ -97,7 +92,7 @@ console.log(`training time: ${performance.now() - time}ms`);
 // Calculate metrics
 const res = await net.predict(tensor2D(test[0]));
 const y1 = encoder.untransform(
-  CategoricalEncoder.fromSoftmax(res as Tensor<2>)
+  CategoricalEncoder.fromSoftmax(res as Tensor<2>),
 );
 const y0 = encoder.untransform(test[1]);
 
@@ -106,5 +101,5 @@ const cMatrix = new ClassificationReport(y0, y1);
 console.log(cMatrix);
 console.log(
   "Total Accuracy: ",
-  y1.filter((x, i) => x === y0[i]).length / y1.length
+  y1.filter((x, i) => x === y0[i]).length / y1.length,
 );
diff --git a/examples/classification/spam.ts b/examples/classification/spam.ts
index 04dacf8..16629f2 100644
--- a/examples/classification/spam.ts
+++ b/examples/classification/spam.ts
@@ -1,4 +1,4 @@
-import { parse } from "https://deno.land/std@0.188.0/csv/parse.ts";
+import { parse } from "jsr:@std/csv@1.0.3/parse";
 import {
   Cost,
   CPU,
@@ -7,18 +7,19 @@ import {
   ReluLayer,
   Sequential,
   setupBackend,
-  SigmoidLayer,
   tensor,
   tensor2D,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
 // Import helpers for metrics
 import {
   ClassificationReport,
-  TextVectorizer,
+  CountVectorizer,
+  SplitTokenizer,
+  TfIdfTransformer,
   // Split the dataset
   useSplit,
-} from "https://deno.land/x/vectorizer@v0.3.7/mod.ts";
+} from "../../packages/utilities/mod.ts";
 
 // Define classes
 const ymap = ["spam", "ham"];
@@ -31,21 +32,25 @@ const data = parse(_data);
 const x = data.map((msg) => msg[1]);
 
 // Get the classes
-const y = data.map((msg) => ymap.indexOf(msg[0]) === 0 ? -1 : 1);
+const y = data.map((msg) => (ymap.indexOf(msg[0]) === 0 ? -1 : 1));
 
 // Split the dataset for training and testing
-const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
-  [typeof x, typeof y],
-  [typeof x, typeof y],
-];
+const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y);
 
 // Vectorize the text messages
-const vec = new TextVectorizer({
-  mode: "tfidf",
-  config: { skipWords: "english", standardize: { lowercase: true } },
+
+const tokenizer = new SplitTokenizer({
+  skipWords: "english",
+  standardize: { lowercase: true },
 }).fit(train[0]);
 
-const x_vec = vec.transform(train[0], "f32");
+const vec = new CountVectorizer(tokenizer.vocabulary.size);
+
+const x_vec = vec.transform(tokenizer.transform(train[0]), "f32")
+
+const tfidf = new TfIdfTransformer();
+
+const x_tfidf = tfidf.fit(x_vec).transform(x_vec)
 
 // Setup the CPU backend for Netsaur
 await setupBackend(CPU);
@@ -72,10 +77,10 @@ const net = new Sequential({
 
   // We are using Log Loss for finding cost
   cost: Cost.Hinge,
-  optimizer: NadamOptimizer()
+  optimizer: NadamOptimizer(),
 });
 
-const inputs = tensor(x_vec.data, x_vec.shape);
+const inputs = tensor(x_tfidf);
 
 const time = performance.now();
 // Train the network
@@ -89,15 +94,15 @@ net.train(
   // Train for 20 epochs
   20,
   2,
-  0.01,
+  0.01
 );
 
 console.log(`training time: ${performance.now() - time}ms`);
 
-const x_vec_test = vec.transform(test[0]);
+const x_vec_test = tfidf.transform(vec.transform(tokenizer.transform(test[0]), "f32"));
 
 // Calculate metrics
-const res = await net.predict(tensor(x_vec_test.data, x_vec_test.shape));
-const y1 = res.data.map((i) => i < 0 ? -1 : 1);
+const res = await net.predict(tensor(x_vec_test));
+const y1 = res.data.map((i) => (i < 0 ? -1 : 1));
 const cMatrix = new ClassificationReport(test[1], y1);
 console.log("Confusion Matrix: ", cMatrix);
diff --git a/examples/filters/conv.ts b/examples/filters/conv.ts
index 47e11d2..4696970 100644
--- a/examples/filters/conv.ts
+++ b/examples/filters/conv.ts
@@ -13,10 +13,10 @@ import {
   setupBackend,
   Tensor,
   tensor4D,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 import { decode } from "https://deno.land/x/pngs@0.1.1/mod.ts";
 import { createCanvas } from "https://deno.land/x/canvas@v1.4.1/mod.ts";
-import type { Layer } from "../../src/core/api/layer.ts";
+import type { Layer } from "../../packages/core/src/core/api/layer.ts";
 
 const canvas = createCanvas(600, 600);
 const ctx = canvas.getContext("2d");
diff --git a/examples/filters/conv_wasm.ts b/examples/filters/conv_wasm.ts
index 4a7291e..1b8aadf 100644
--- a/examples/filters/conv_wasm.ts
+++ b/examples/filters/conv_wasm.ts
@@ -13,10 +13,10 @@ import {
   Tensor,
   tensor4D,
   WASM,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 import { decode } from "https://deno.land/x/pngs@0.1.1/mod.ts";
 import { createCanvas } from "https://deno.land/x/canvas@v1.4.1/mod.ts";
-import type { Layer } from "../../src/core/api/layer.ts";
+import type { Layer } from "../../packages/core/src/core/api/layer.ts";
 
 const canvas = createCanvas(600, 600);
 const ctx = canvas.getContext("2d");
diff --git a/examples/linear.ts b/examples/linear.ts
index 70b8015..ac287f4 100644
--- a/examples/linear.ts
+++ b/examples/linear.ts
@@ -9,7 +9,7 @@ import {
   Sequential,
   setupBackend,
   tensor2D,
-} from "../mod.ts";
+} from "../packages/core/mod.ts";
 
 /**
  * The test data used for predicting the output of the function y = 2x + 1
diff --git a/examples/mnist/common.ts b/examples/mnist/common.ts
index 236ea6a..41b75a2 100644
--- a/examples/mnist/common.ts
+++ b/examples/mnist/common.ts
@@ -1,5 +1,4 @@
-import type { DataSet } from "../../src/core/types.ts";
-import { Tensor } from "../../mod.ts";
+import { Tensor, type DataSet } from "../../packages/core/mod.ts";
 
 export function assert(condition: boolean, message?: string) {
   if (!condition) {
diff --git a/examples/mnist/predict.ts b/examples/mnist/predict.ts
index e6b8553..321a042 100644
--- a/examples/mnist/predict.ts
+++ b/examples/mnist/predict.ts
@@ -6,7 +6,7 @@ import {
   type Shape,
   type Tensor,
   tensor,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 import { loadDataset } from "./common.ts";
 
 await setupBackend(CPU);
diff --git a/examples/mnist/train.ts b/examples/mnist/train.ts
index ae22f79..61013c8 100644
--- a/examples/mnist/train.ts
+++ b/examples/mnist/train.ts
@@ -10,7 +10,7 @@ import {
   Sequential,
   setupBackend,
   SoftmaxLayer,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
 import { loadDataset } from "./common.ts";
 
diff --git a/examples/mnist/train_batchnorm.ts b/examples/mnist/train_batchnorm.ts
index 6461153..0ba5815 100644
--- a/examples/mnist/train_batchnorm.ts
+++ b/examples/mnist/train_batchnorm.ts
@@ -11,7 +11,7 @@ import {
   Sequential,
   setupBackend,
   SoftmaxLayer,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
 import { loadDataset } from "./common.ts";
 
diff --git a/examples/mnist/train_dropout.ts b/examples/mnist/train_dropout.ts
index b1570fc..94ddbc7 100644
--- a/examples/mnist/train_dropout.ts
+++ b/examples/mnist/train_dropout.ts
@@ -11,7 +11,7 @@ import {
   Sequential,
   setupBackend,
   SoftmaxLayer,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
 import { loadDataset } from "./common.ts";
 
diff --git a/examples/model/run.ts b/examples/model/run.ts
index adbfacc..be9b7c5 100644
--- a/examples/model/run.ts
+++ b/examples/model/run.ts
@@ -1,4 +1,4 @@
-import { CPU, Sequential, setupBackend, tensor2D } from "../../mod.ts";
+import { CPU, Sequential, setupBackend, tensor2D } from "../../packages/core/mod.ts";
 
 /**
  * Setup the CPU backend. This backend is fast but doesn't work on the Edge.
diff --git a/examples/model/train.ts b/examples/model/train.ts
index 49350c6..76f7a72 100644
--- a/examples/model/train.ts
+++ b/examples/model/train.ts
@@ -6,7 +6,7 @@ import {
   setupBackend,
   SigmoidLayer,
   tensor2D,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
 /**
  * Setup the CPU backend. This backend is fast but doesn't work on the Edge.
diff --git a/examples/multiple-linear/student.ts b/examples/multiple-linear/student.ts
index 12e88b7..bc6eb13 100644
--- a/examples/multiple-linear/student.ts
+++ b/examples/multiple-linear/student.ts
@@ -6,14 +6,13 @@ import {
   OneCycle,
   Sequential,
   setupBackend,
-  tensor1D,
   tensor2D,
-} from "../../mod.ts";
+} from "../../packages/core/mod.ts";
 
-import { parse } from "https://deno.land/std@0.214.0/csv/parse.ts";
+import { parse } from "jsr:@std/csv@1.0.3/parse";
 
 // Import helpers for splitting dataset
-import { useSplit } from "https://deno.land/x/vectorizer@v0.2.1/mod.ts";
+import { useSplit } from "../../packages/utilities/mod.ts";
 
 // Read the training dataset
 const _data = Deno.readTextFileSync("examples/multiple-linear/student.csv");
@@ -36,7 +35,7 @@ const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
 // Setup the CPU backend for Netsaur
 await setupBackend(CPU);
 
-console.log(train)
+console.log(train);
 
 // Create a sequential neural network
 const net = new Sequential({
diff --git a/examples/tokenizers/basic.ts b/examples/tokenizers/basic.ts
index 916daad..747f5ae 100644
--- a/examples/tokenizers/basic.ts
+++ b/examples/tokenizers/basic.ts
@@ -1,4 +1,4 @@
-import { init, Tokenizer } from "../../tokenizers/mod.ts";
+import { init, Tokenizer } from "../../packages/tokenizers/mod.ts";
 
 await init();
 
diff --git a/examples/xor_auto.ts b/examples/xor_auto.ts
index 973ca6b..cc072ba 100644
--- a/examples/xor_auto.ts
+++ b/examples/xor_auto.ts
@@ -10,7 +10,7 @@ import {
   setupBackend,
   SigmoidLayer,
   tensor2D,
-} from "../mod.ts";
+} from "../packages/core/mod.ts";
 
 /**
  * Setup the AUTO backend. This backend is chosen automatically based on the environment.
diff --git a/examples/xor_cpu.ts b/examples/xor_cpu.ts
index 214b49d..0081393 100644
--- a/examples/xor_cpu.ts
+++ b/examples/xor_cpu.ts
@@ -10,7 +10,7 @@ import {
   setupBackend,
   SigmoidLayer,
   tensor2D,
-} from "../mod.ts";
+} from "../packages/core/mod.ts";
 
 /**
  * Setup the CPU backend. This backend is fast but doesn't work on the Edge.
diff --git a/examples/xor_gpu.ts b/examples/xor_gpu.ts
index 81c6ee5..6bdc150 100644
--- a/examples/xor_gpu.ts
+++ b/examples/xor_gpu.ts
@@ -10,7 +10,7 @@ import {
   setupBackend,
   SigmoidLayer,
   tensor2D,
-} from "../mod.ts";
+} from "../packages/core/mod.ts";
 
 /**
  * Setup the GPU backend. This backend is fast but doesn't work on the Edge.
diff --git a/examples/xor_option.ts b/examples/xor_option.ts
index caae41c..e838669 100644
--- a/examples/xor_option.ts
+++ b/examples/xor_option.ts
@@ -12,7 +12,7 @@ import {
   SigmoidLayer,
   tensor2D,
   WASM,
-} from "../mod.ts";
+} from "../packages/core/mod.ts";
 
 /**
  * Setup the backend with the OPTION function. The backend is chosen automatically based on the environment.
diff --git a/examples/xor_wasm.ts b/examples/xor_wasm.ts
index 6bd87a9..b1d61df 100644
--- a/examples/xor_wasm.ts
+++ b/examples/xor_wasm.ts
@@ -10,7 +10,7 @@ import {
   SigmoidLayer,
   tensor2D,
   WASM,
-} from "../mod.ts";
+} from "../packages/core/mod.ts";
 
 /**
  * Setup the WASM backend. This backend is slower than the CPU backend but works on the Edge.
diff --git a/mod.ts b/mod.ts
index fad38c3..5b77cd7 100644
--- a/mod.ts
+++ b/mod.ts
@@ -1,17 +1,17 @@
-export { setupBackend } from "./src/core/engine.ts";
-export * from "./src/core/mod.ts";
-export * from "./src/core/types.ts";
-export * from "./src/core/tensor/tensor.ts";
-export * from "./src/core/api/layers.ts";
-export * from "./src/core/api/shape.ts";
-export * from "./src/core/api/network.ts";
-export * from "./src/core/api/optimizer.ts";
-export * from "./src/core/api/scheduler.ts";
-export { GPU } from "./src/backends/gpu/mod.ts";
+export { setupBackend } from "./packages/core/core/engine.ts";
+export * from "./packages/core/core/mod.ts";
+export * from "./packages/core/core/types.ts";
+export * from "./packages/core/core/tensor/tensor.ts";
+export * from "./packages/core/core/api/layers.ts";
+export * from "./packages/core/core/api/shape.ts";
+export * from "./packages/core/core/api/network.ts";
+export * from "./packages/core/core/api/optimizer.ts";
+export * from "./packages/core/core/api/scheduler.ts";
+export { GPU } from "./packages/core/backends/gpu/mod.ts";
 
-import { CPU, type CPUBackendLoader } from "./src/backends/cpu/mod.ts";
-import { WASM, type WASMBackendLoader } from "./src/backends/wasm/mod.ts";
-import type { BackendLoader } from "./src/core/engine.ts";
+import { CPU, type CPUBackendLoader } from "./packages/core/backends/cpu/mod.ts";
+import { WASM, type WASMBackendLoader } from "./packages/core/backends/wasm/mod.ts";
+import type { BackendLoader } from "./packages/core/core/engine.ts";
 
 onerror = () => {
   if (typeof Deno == "undefined") {
diff --git a/src/.DS_Store b/packages/core/.DS_Store
similarity index 100%
rename from src/.DS_Store
rename to packages/core/.DS_Store
diff --git a/packages/core/mod.ts b/packages/core/mod.ts
new file mode 100644
index 0000000..9b52ef0
--- /dev/null
+++ b/packages/core/mod.ts
@@ -0,0 +1,10 @@
+export { setupBackend } from "./src/core/engine.ts";
+export * from "./src/core/mod.ts";
+export * from "./src/core/types.ts";
+export * from "./src/core/tensor/tensor.ts";
+export * from "./src/core/api/layers.ts";
+export * from "./src/core/api/shape.ts";
+export * from "./src/core/api/network.ts";
+export * from "./src/core/api/optimizer.ts";
+export * from "./src/core/api/scheduler.ts";
+export * from "./src/backend_loader.ts"
\ No newline at end of file
diff --git a/packages/core/src/backend_loader.ts b/packages/core/src/backend_loader.ts
new file mode 100644
index 0000000..4ab5c3e
--- /dev/null
+++ b/packages/core/src/backend_loader.ts
@@ -0,0 +1,33 @@
+export { GPU } from "./backends/gpu/mod.ts";
+
+import { CPU, type CPUBackendLoader } from "./backends/cpu/mod.ts";
+import { WASM, type WASMBackendLoader } from "./backends/wasm/mod.ts";
+import type { BackendLoader } from "./core/engine.ts";
+
+onerror = () => {
+  if (typeof Deno == "undefined") {
+    throw new Error(
+      "Warning: Deno is not defined. Did you mean to import from ./web.ts instead of ./mod.ts?",
+    );
+  }
+};
+
+/**
+ * The AUTO backend is chosen automatically based on the environment.
+ */
+const AUTO: WASMBackendLoader | CPUBackendLoader = Deno.dlopen === undefined
+  ? WASM
+  : CPU;
+
+/**
+ * The OPTION function is used to choose a backend from a list of options.
+ */
+export function OPTION(...backends: BackendLoader[]): BackendLoader {
+  for (const backend of backends) {
+    if (backend.isSupported()) {
+      return backend;
+    }
+  }
+  throw new Error("No provided backend is supported");
+}
+export { AUTO, CPU, WASM };
diff --git a/src/backends/.DS_Store b/packages/core/src/backends/.DS_Store
similarity index 100%
rename from src/backends/.DS_Store
rename to packages/core/src/backends/.DS_Store
diff --git a/src/backends/cpu/backend.ts b/packages/core/src/backends/cpu/backend.ts
similarity index 100%
rename from src/backends/cpu/backend.ts
rename to packages/core/src/backends/cpu/backend.ts
diff --git a/src/backends/cpu/mod.ts b/packages/core/src/backends/cpu/mod.ts
similarity index 97%
rename from src/backends/cpu/mod.ts
rename to packages/core/src/backends/cpu/mod.ts
index 11bd85a..e640f38 100644
--- a/src/backends/cpu/mod.ts
+++ b/packages/core/src/backends/cpu/mod.ts
@@ -1,4 +1,4 @@
-import { dlopen, type FetchOptions } from "../../../deps.ts";
+import { dlopen, type FetchOptions } from "../../../../../deps.ts";
 import { CPUBackend } from "./backend.ts";
 import { NoBackendError } from "../../core/api/error.ts";
 import { type BackendLoader, Engine } from "../../core/engine.ts";
diff --git a/src/backends/cpu/util.ts b/packages/core/src/backends/cpu/util.ts
similarity index 100%
rename from src/backends/cpu/util.ts
rename to packages/core/src/backends/cpu/util.ts
diff --git a/src/backends/gpu/backend.ts b/packages/core/src/backends/gpu/backend.ts
similarity index 100%
rename from src/backends/gpu/backend.ts
rename to packages/core/src/backends/gpu/backend.ts
diff --git a/src/backends/gpu/mod.ts b/packages/core/src/backends/gpu/mod.ts
similarity index 97%
rename from src/backends/gpu/mod.ts
rename to packages/core/src/backends/gpu/mod.ts
index ddb1c08..a98b4f3 100644
--- a/src/backends/gpu/mod.ts
+++ b/packages/core/src/backends/gpu/mod.ts
@@ -1,4 +1,4 @@
-import { dlopen, type FetchOptions } from "../../../deps.ts";
+import { dlopen, type FetchOptions } from "../../../../../deps.ts";
 import { GPUBackend } from "./backend.ts";
 import { NoBackendError } from "../../core/api/error.ts";
 import { type BackendLoader, Engine } from "../../core/engine.ts";
diff --git a/src/backends/gpu/util.ts b/packages/core/src/backends/gpu/util.ts
similarity index 100%
rename from src/backends/gpu/util.ts
rename to packages/core/src/backends/gpu/util.ts
diff --git a/src/backends/wasm/backend.ts b/packages/core/src/backends/wasm/backend.ts
similarity index 100%
rename from src/backends/wasm/backend.ts
rename to packages/core/src/backends/wasm/backend.ts
diff --git a/src/backends/wasm/lib/netsaur.generated.d.ts b/packages/core/src/backends/wasm/lib/netsaur.generated.d.ts
similarity index 100%
rename from src/backends/wasm/lib/netsaur.generated.d.ts
rename to packages/core/src/backends/wasm/lib/netsaur.generated.d.ts
diff --git a/src/backends/wasm/lib/netsaur.generated.js b/packages/core/src/backends/wasm/lib/netsaur.generated.js
similarity index 99%
rename from src/backends/wasm/lib/netsaur.generated.js
rename to packages/core/src/backends/wasm/lib/netsaur.generated.js
index de1703a..05940c6 100644
--- a/src/backends/wasm/lib/netsaur.generated.js
+++ b/packages/core/src/backends/wasm/lib/netsaur.generated.js
@@ -4,7 +4,7 @@
 // deno-fmt-ignore-file
 /// <reference types="./netsaur.generated.d.ts" />
 
-// source-hash: af6cb6fb1e5d51212d81be60c88a3b2eb20dfbc5
+// source-hash: 24bcfd72e3631f0f05524ea722a11a2b75b29185
 let wasm;
 let cachedInt32Memory0;
 
@@ -12,12 +12,21 @@ const heap = new Array(128).fill(undefined);
 
 heap.push(undefined, null, true, false);
 
+let heap_next = heap.length;
+
+function addHeapObject(obj) {
+  if (heap_next === heap.length) heap.push(heap.length + 1);
+  const idx = heap_next;
+  heap_next = heap[idx];
+
+  heap[idx] = obj;
+  return idx;
+}
+
 function getObject(idx) {
   return heap[idx];
 }
 
-let heap_next = heap.length;
-
 function dropObject(idx) {
   if (idx < 132) return;
   heap[idx] = heap_next;
@@ -30,15 +39,6 @@ function takeObject(idx) {
   return ret;
 }
 
-function addHeapObject(obj) {
-  if (heap_next === heap.length) heap.push(heap.length + 1);
-  const idx = heap_next;
-  heap_next = heap[idx];
-
-  heap[idx] = obj;
-  return idx;
-}
-
 const cachedTextDecoder = typeof TextDecoder !== "undefined"
   ? new TextDecoder("utf-8", { ignoreBOM: true, fatal: true })
   : {
@@ -214,16 +214,16 @@ function handleError(f, args) {
 
 const imports = {
   __wbindgen_placeholder__: {
-    __wbindgen_object_drop_ref: function (arg0) {
-      takeObject(arg0);
-    },
-    __wbg_log_67e9366454666686: function (arg0, arg1) {
+    __wbg_log_023d7669e382bddf: function (arg0, arg1) {
       console.log(getStringFromWasm0(arg0, arg1));
     },
     __wbindgen_number_new: function (arg0) {
       const ret = arg0;
       return addHeapObject(ret);
     },
+    __wbindgen_object_drop_ref: function (arg0) {
+      takeObject(arg0);
+    },
     __wbg_crypto_c48a774b022d20ac: function (arg0) {
       const ret = getObject(arg0).crypto;
       return addHeapObject(ret);
@@ -249,10 +249,6 @@ const imports = {
       const ret = typeof (getObject(arg0)) === "string";
       return ret;
     },
-    __wbg_msCrypto_bcb970640f50a1e8: function (arg0) {
-      const ret = getObject(arg0).msCrypto;
-      return addHeapObject(ret);
-    },
     __wbg_require_8f08ceecec0f4fee: function () {
       return handleError(function () {
         const ret = module.require;
@@ -267,6 +263,10 @@ const imports = {
       const ret = getStringFromWasm0(arg0, arg1);
       return addHeapObject(ret);
     },
+    __wbg_msCrypto_bcb970640f50a1e8: function (arg0) {
+      const ret = getObject(arg0).msCrypto;
+      return addHeapObject(ret);
+    },
     __wbg_randomFillSync_dc1e9a60c158336d: function () {
       return handleError(function (arg0, arg1) {
         getObject(arg0).randomFillSync(takeObject(arg1));
diff --git a/packages/core/src/backends/wasm/lib/netsaur_bg.wasm b/packages/core/src/backends/wasm/lib/netsaur_bg.wasm
new file mode 100644
index 0000000..46420ab
Binary files /dev/null and b/packages/core/src/backends/wasm/lib/netsaur_bg.wasm differ
diff --git a/src/backends/wasm/mod.ts b/packages/core/src/backends/wasm/mod.ts
similarity index 100%
rename from src/backends/wasm/mod.ts
rename to packages/core/src/backends/wasm/mod.ts
diff --git a/src/backends/wasm/utils.ts b/packages/core/src/backends/wasm/utils.ts
similarity index 100%
rename from src/backends/wasm/utils.ts
rename to packages/core/src/backends/wasm/utils.ts
diff --git a/src/core/api/error.ts b/packages/core/src/core/api/error.ts
similarity index 100%
rename from src/core/api/error.ts
rename to packages/core/src/core/api/error.ts
diff --git a/src/core/api/layer.ts b/packages/core/src/core/api/layer.ts
similarity index 100%
rename from src/core/api/layer.ts
rename to packages/core/src/core/api/layer.ts
diff --git a/src/core/api/layers.ts b/packages/core/src/core/api/layers.ts
similarity index 100%
rename from src/core/api/layers.ts
rename to packages/core/src/core/api/layers.ts
diff --git a/src/core/api/network.ts b/packages/core/src/core/api/network.ts
similarity index 100%
rename from src/core/api/network.ts
rename to packages/core/src/core/api/network.ts
diff --git a/src/core/api/optimizer.ts b/packages/core/src/core/api/optimizer.ts
similarity index 93%
rename from src/core/api/optimizer.ts
rename to packages/core/src/core/api/optimizer.ts
index 739cfc9..c8ef7b1 100644
--- a/src/core/api/optimizer.ts
+++ b/packages/core/src/core/api/optimizer.ts
@@ -35,7 +35,9 @@ export function NadamOptimizer(config: AdamOptimizerConfig = {}): Optimizer {
   return { type: OptimizerType.Nadam, config };
 }
 
-export function RMSPropOptimizer(config: RMSPropOptimizerConfig = {}): Optimizer {
+export function RMSPropOptimizer(
+  config: RMSPropOptimizerConfig = {},
+): Optimizer {
   config.decayRate = config.decayRate || 0.9;
   config.epsilon = config.epsilon || 1e-8;
   return { type: OptimizerType.RMSProp, config };
diff --git a/src/core/api/scheduler.ts b/packages/core/src/core/api/scheduler.ts
similarity index 100%
rename from src/core/api/scheduler.ts
rename to packages/core/src/core/api/scheduler.ts
diff --git a/src/core/api/shape.ts b/packages/core/src/core/api/shape.ts
similarity index 100%
rename from src/core/api/shape.ts
rename to packages/core/src/core/api/shape.ts
diff --git a/src/core/engine.ts b/packages/core/src/core/engine.ts
similarity index 100%
rename from src/core/engine.ts
rename to packages/core/src/core/engine.ts
diff --git a/src/core/mod.ts b/packages/core/src/core/mod.ts
similarity index 100%
rename from src/core/mod.ts
rename to packages/core/src/core/mod.ts
diff --git a/src/core/tensor/tensor.ts b/packages/core/src/core/tensor/tensor.ts
similarity index 100%
rename from src/core/tensor/tensor.ts
rename to packages/core/src/core/tensor/tensor.ts
diff --git a/src/core/tensor/util.ts b/packages/core/src/core/tensor/util.ts
similarity index 100%
rename from src/core/tensor/util.ts
rename to packages/core/src/core/tensor/util.ts
diff --git a/src/core/types.ts b/packages/core/src/core/types.ts
similarity index 98%
rename from src/core/types.ts
rename to packages/core/src/core/types.ts
index 607f34e..7901fd9 100644
--- a/src/core/types.ts
+++ b/packages/core/src/core/types.ts
@@ -17,7 +17,7 @@ export interface Backend {
     datasets: DataSet[],
     epochs: number,
     batches: number,
-    rate: number
+    rate: number,
   ): void;
 
   /**
@@ -28,7 +28,7 @@ export interface Backend {
   predict(
     input: Tensor<Rank>,
     layers?: number[],
-    outputShape?: Shape<Rank>
+    outputShape?: Shape<Rank>,
   ): Promise<Tensor<Rank>>;
 
   /**
@@ -161,13 +161,13 @@ export enum Cost {
   MAE = "mae",
 
   /**
-   * Huber is a cost function for regression and is less sensitive to outliers than the 
+   * Huber is a cost function for regression and is less sensitive to outliers than the
    * squared error loss
    */
   Huber = "huber",
 
   /**
-   * Smoothed hinge is a variant of the Huber cost function used for binary classification. 
+   * Smoothed hinge is a variant of the Huber cost function used for binary classification.
    * It is a smoothed version of hinge and is more robust to outliers.
    */
   SmoothHinge = "smoothhinge",
diff --git a/src/plugins/mod.ts b/packages/core/src/plugins/mod.ts
similarity index 80%
rename from src/plugins/mod.ts
rename to packages/core/src/plugins/mod.ts
index b094f55..9f9e8ff 100644
--- a/src/plugins/mod.ts
+++ b/packages/core/src/plugins/mod.ts
@@ -1,5 +1,5 @@
 import type { Plugin } from "./types.ts";
-import type { NeuralNetwork } from "../../mod.ts";
+import type { NeuralNetwork } from "../../../../mod.ts";
 
 /**
  * Load a plugin into a NeuralNetwork instance.
diff --git a/src/plugins/types.ts b/packages/core/src/plugins/types.ts
similarity index 100%
rename from src/plugins/types.ts
rename to packages/core/src/plugins/types.ts
diff --git a/data/data.ts b/packages/data/data.ts
similarity index 91%
rename from data/data.ts
rename to packages/data/data.ts
index 0cfb8d3..3db7bd7 100644
--- a/data/data.ts
+++ b/packages/data/data.ts
@@ -1,4 +1,4 @@
-import type { Rank, Tensor } from "../mod.ts";
+import type { Rank, Tensor } from "../core/mod.ts";
 import { type CsvLoaderConfig, loadCsv } from "./datasets/csv.ts";
 import type { DataLike } from "./types.ts";
 
diff --git a/data/datasets/csv.ts b/packages/data/datasets/csv.ts
similarity index 97%
rename from data/datasets/csv.ts
rename to packages/data/datasets/csv.ts
index ded3a5b..869b2a0 100644
--- a/data/datasets/csv.ts
+++ b/packages/data/datasets/csv.ts
@@ -1,4 +1,4 @@
-import { tensor2D } from "../../mod.ts";
+import { tensor2D } from "../../core/mod.ts";
 import type { DataLike } from "../types.ts";
 import { CsvParseStream } from "../deps.ts";
 
diff --git a/data/datasets/text.ts b/packages/data/datasets/text.ts
similarity index 100%
rename from data/datasets/text.ts
rename to packages/data/datasets/text.ts
diff --git a/packages/data/deps.ts b/packages/data/deps.ts
new file mode 100644
index 0000000..3532b0b
--- /dev/null
+++ b/packages/data/deps.ts
@@ -0,0 +1 @@
+export { CsvParseStream } from "jsr:@std/csv@1.0.3";
diff --git a/data/mod.ts b/packages/data/mod.ts
similarity index 100%
rename from data/mod.ts
rename to packages/data/mod.ts
diff --git a/data/types.ts b/packages/data/types.ts
similarity index 85%
rename from data/types.ts
rename to packages/data/types.ts
index 52a951c..ee2f076 100644
--- a/data/types.ts
+++ b/packages/data/types.ts
@@ -1,4 +1,4 @@
-import type { Rank, Tensor } from "../mod.ts";
+import type { Rank, Tensor } from "../core/mod.ts";
 
 export interface DataLike {
   /**
diff --git a/tokenizers/lib/netsaur_tokenizers.generated.d.ts b/packages/tokenizers/lib/netsaur_tokenizers.generated.d.ts
similarity index 100%
rename from tokenizers/lib/netsaur_tokenizers.generated.d.ts
rename to packages/tokenizers/lib/netsaur_tokenizers.generated.d.ts
diff --git a/tokenizers/lib/netsaur_tokenizers.generated.js b/packages/tokenizers/lib/netsaur_tokenizers.generated.js
similarity index 99%
rename from tokenizers/lib/netsaur_tokenizers.generated.js
rename to packages/tokenizers/lib/netsaur_tokenizers.generated.js
index 3f7591b..97e299c 100644
--- a/tokenizers/lib/netsaur_tokenizers.generated.js
+++ b/packages/tokenizers/lib/netsaur_tokenizers.generated.js
@@ -4,7 +4,7 @@
 // deno-fmt-ignore-file
 /// <reference types="./netsaur_tokenizers.generated.d.ts" />
 
-// source-hash: 1c51c6670646b6ae9e407e9c7104be0bc13ca7ad
+// source-hash: 7411764ea128cc65962626e00ed109dfa6e5ba46
 let wasm;
 
 const heap = new Array(128).fill(undefined);
@@ -380,23 +380,23 @@ const imports = {
       const ret = new Error(getStringFromWasm0(arg0, arg1));
       return addHeapObject(ret);
     },
+    __wbindgen_number_new: function (arg0) {
+      const ret = arg0;
+      return addHeapObject(ret);
+    },
+    __wbindgen_string_new: function (arg0, arg1) {
+      const ret = getStringFromWasm0(arg0, arg1);
+      return addHeapObject(ret);
+    },
     __wbindgen_is_object: function (arg0) {
       const val = getObject(arg0);
       const ret = typeof val === "object" && val !== null;
       return ret;
     },
-    __wbindgen_number_new: function (arg0) {
-      const ret = arg0;
-      return addHeapObject(ret);
-    },
     __wbindgen_object_clone_ref: function (arg0) {
       const ret = getObject(arg0);
       return addHeapObject(ret);
     },
-    __wbindgen_string_new: function (arg0, arg1) {
-      const ret = getStringFromWasm0(arg0, arg1);
-      return addHeapObject(ret);
-    },
     __wbg_set_bd72c078edfa51ad: function (arg0, arg1, arg2) {
       getObject(arg0)[takeObject(arg1)] = takeObject(arg2);
     },
@@ -416,10 +416,6 @@ const imports = {
       const ret = getObject(arg0).node;
       return addHeapObject(ret);
     },
-    __wbg_msCrypto_bcb970640f50a1e8: function (arg0) {
-      const ret = getObject(arg0).msCrypto;
-      return addHeapObject(ret);
-    },
     __wbg_require_8f08ceecec0f4fee: function () {
       return handleError(function () {
         const ret = module.require;
@@ -430,6 +426,10 @@ const imports = {
       const ret = typeof (getObject(arg0)) === "function";
       return ret;
     },
+    __wbg_msCrypto_bcb970640f50a1e8: function (arg0) {
+      const ret = getObject(arg0).msCrypto;
+      return addHeapObject(ret);
+    },
     __wbg_randomFillSync_dc1e9a60c158336d: function () {
       return handleError(function (arg0, arg1) {
         getObject(arg0).randomFillSync(takeObject(arg1));
diff --git a/packages/tokenizers/lib/netsaur_tokenizers_bg.wasm b/packages/tokenizers/lib/netsaur_tokenizers_bg.wasm
new file mode 100644
index 0000000..3181915
Binary files /dev/null and b/packages/tokenizers/lib/netsaur_tokenizers_bg.wasm differ
diff --git a/tokenizers/mod.ts b/packages/tokenizers/mod.ts
similarity index 100%
rename from tokenizers/mod.ts
rename to packages/tokenizers/mod.ts
diff --git a/packages/utilities/examples/encoding/categorical.ipynb b/packages/utilities/examples/encoding/categorical.ipynb
new file mode 100644
index 0000000..0150430
--- /dev/null
+++ b/packages/utilities/examples/encoding/categorical.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import { CategoricalEncoder } from \"../../src/encoding/categorical.ts\";\n",
+    "import { parse } from \"jsr:@std/csv@1.0.3/parse\";"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.7\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.6\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.4\"\u001b[39m, \u001b[32m\"3.9\"\u001b[39m, \u001b[32m\"1.7\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.1\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.4\"\u001b[39m, \u001b[32m\"3.7\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.8\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.8\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.1\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.3\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.1\"\u001b[39m, \u001b[32m\"0.1\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.8\"\u001b[39m, \u001b[32m\"4.0\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.4\"\u001b[39m, \u001b[32m\"3.9\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"3.8\"\u001b[39m, \u001b[32m\"1.7\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.8\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.4\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.7\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.7\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"3.6\"\u001b[39m, \u001b[32m\"1.0\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.3\"\u001b[39m, \u001b[32m\"1.7\"\u001b[39m, \u001b[32m\"0.5\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.8\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.9\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.2\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.2\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.7\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.8\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.4\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.2\"\u001b[39m, \u001b[32m\"4.1\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.1\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"4.2\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.1\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.1\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"2.3\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.6\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.8\"\u001b[39m, \u001b[32m\"1.9\"\u001b[39m, \u001b[32m\"0.4\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.8\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.3\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"3.8\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.3\"\u001b[39m, \u001b[32m\"3.7\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"3.3\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"0.2\"\u001b[39m, \u001b[32m\"setosa\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"7.0\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"4.7\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.4\"\u001b[39m, \u001b[32m\"3.2\"\u001b[39m, \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.9\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"2.3\"\u001b[39m, \u001b[32m\"4.0\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.5\"\u001b[39m, \u001b[32m\"2.8\"\u001b[39m, \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"2.8\"\u001b[39m, \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
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+       "  [ \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"2.4\"\u001b[39m, \u001b[32m\"3.3\"\u001b[39m, \u001b[32m\"1.0\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.6\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
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+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"2.0\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.0\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
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+       "  [ \u001b[32m\"6.1\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"4.7\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.6\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"3.6\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
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+       "  [ \u001b[32m\"6.2\"\u001b[39m, \u001b[32m\"2.2\"\u001b[39m, \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
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+       "  [ \u001b[32m\"6.1\"\u001b[39m, \u001b[32m\"2.8\"\u001b[39m, \u001b[32m\"4.0\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.3\"\u001b[39m, \u001b[32m\"2.5\"\u001b[39m, \u001b[32m\"4.9\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.1\"\u001b[39m, \u001b[32m\"2.8\"\u001b[39m, \u001b[32m\"4.7\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.4\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"4.3\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.6\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.8\"\u001b[39m, \u001b[32m\"2.8\"\u001b[39m, \u001b[32m\"4.8\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.7\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"1.7\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.0\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"2.6\"\u001b[39m, \u001b[32m\"3.5\"\u001b[39m, \u001b[32m\"1.0\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"2.4\"\u001b[39m, \u001b[32m\"3.8\"\u001b[39m, \u001b[32m\"1.1\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"2.4\"\u001b[39m, \u001b[32m\"3.7\"\u001b[39m, \u001b[32m\"1.0\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.8\"\u001b[39m, \u001b[32m\"2.7\"\u001b[39m, \u001b[32m\"3.9\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.0\"\u001b[39m, \u001b[32m\"2.7\"\u001b[39m, \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.4\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.0\"\u001b[39m, \u001b[32m\"3.4\"\u001b[39m, \u001b[32m\"4.5\"\u001b[39m, \u001b[32m\"1.6\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.7\"\u001b[39m, \u001b[32m\"3.1\"\u001b[39m, \u001b[32m\"4.7\"\u001b[39m, \u001b[32m\"1.5\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.3\"\u001b[39m, \u001b[32m\"2.3\"\u001b[39m, \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.6\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"4.1\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"2.5\"\u001b[39m, \u001b[32m\"4.0\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.5\"\u001b[39m, \u001b[32m\"2.6\"\u001b[39m, \u001b[32m\"4.4\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.1\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"4.6\"\u001b[39m, \u001b[32m\"1.4\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.8\"\u001b[39m, \u001b[32m\"2.6\"\u001b[39m, \u001b[32m\"4.0\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.0\"\u001b[39m, \u001b[32m\"2.3\"\u001b[39m, \u001b[32m\"3.3\"\u001b[39m, \u001b[32m\"1.0\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.6\"\u001b[39m, \u001b[32m\"2.7\"\u001b[39m, \u001b[32m\"4.2\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"4.2\"\u001b[39m, \u001b[32m\"1.2\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"4.2\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"6.2\"\u001b[39m, \u001b[32m\"2.9\"\u001b[39m, \u001b[32m\"4.3\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.1\"\u001b[39m, \u001b[32m\"2.5\"\u001b[39m, \u001b[32m\"3.0\"\u001b[39m, \u001b[32m\"1.1\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  [ \u001b[32m\"5.7\"\u001b[39m, \u001b[32m\"2.8\"\u001b[39m, \u001b[32m\"4.1\"\u001b[39m, \u001b[32m\"1.3\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m ],\n",
+       "  ... 50 more items\n",
+       "]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// Read the training dataset\n",
+    "const _data = Deno.readTextFileSync(\"iris.csv\");\n",
+    "const data = parse(_data);\n",
+    "data;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const cat = new CategoricalEncoder();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  ... 50 more items\n",
+       "]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "const targets = data.map((x) => x[4]);\n",
+    "targets;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const res = cat.fit(targets).transform(targets, \"f32\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th>idx</th><th>0</th><th>1</th><th>2</th></tr></thead><tr><td><strong>0</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>1</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>2</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>3</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>4</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>5</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>6</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>7</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>8</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>9</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>10</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>11</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>12</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>13</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>14</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>15</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>16</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>17</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>18</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>19</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>20</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>21</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>22</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>23</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>24</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>25</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>26</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>27</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>28</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>29</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>30</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>31</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>32</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>33</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>34</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>35</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>36</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>37</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>38</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>39</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>40</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>41</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>42</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>43</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>44</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>45</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>46</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>47</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>48</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>49</strong></td><td>1</td><td>0</td><td>0</td></tr><tr><td><strong>50</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>51</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>52</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>53</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>54</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>55</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>56</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>57</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>58</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>59</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>60</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>61</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>62</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>63</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>64</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>65</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>66</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>67</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>68</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>69</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>70</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>71</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>72</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>73</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>74</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>75</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>76</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>77</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>78</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>79</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>80</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>81</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>82</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>83</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>84</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>85</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>86</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>87</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>88</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>89</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>90</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>91</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>92</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>93</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>94</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>95</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>96</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>97</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>98</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>99</strong></td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>100</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>101</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>102</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>103</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>104</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>105</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>106</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>107</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>108</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>109</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>110</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>111</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>112</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>113</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>114</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>115</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>116</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>117</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>118</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>119</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>120</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>121</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>122</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>123</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>124</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>125</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>126</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>127</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>128</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>129</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>130</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>131</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>132</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>133</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>134</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>135</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>136</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>137</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>138</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>139</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>140</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>141</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>142</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>143</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>144</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>145</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>146</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>147</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>148</strong></td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>149</strong></td><td>0</td><td>0</td><td>1</td></tr></table>"
+      ],
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+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,     \u001b[32m\"setosa\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m, \u001b[32m\"versicolor\"\u001b[39m,\n",
+       "  ... 50 more items\n",
+       "]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat.untransform(res);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Deno",
+   "language": "typescript",
+   "name": "deno"
+  },
+  "language_info": {
+   "file_extension": ".ts",
+   "mimetype": "text/x.typescript",
+   "name": "typescript",
+   "nb_converter": "script",
+   "pygments_lexer": "typescript",
+   "version": "5.3.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/packages/utilities/examples/encoding/iris.csv b/packages/utilities/examples/encoding/iris.csv
new file mode 100644
index 0000000..876131d
--- /dev/null
+++ b/packages/utilities/examples/encoding/iris.csv
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,setosa
+4.9,3.0,1.4,0.2,setosa
+4.7,3.2,1.3,0.2,setosa
+4.6,3.1,1.5,0.2,setosa
+5.0,3.6,1.4,0.2,setosa
+5.4,3.9,1.7,0.4,setosa
+4.6,3.4,1.4,0.3,setosa
+5.0,3.4,1.5,0.2,setosa
+4.4,2.9,1.4,0.2,setosa
+4.9,3.1,1.5,0.1,setosa
+5.4,3.7,1.5,0.2,setosa
+4.8,3.4,1.6,0.2,setosa
+4.8,3.0,1.4,0.1,setosa
+4.3,3.0,1.1,0.1,setosa
+5.8,4.0,1.2,0.2,setosa
+5.7,4.4,1.5,0.4,setosa
+5.4,3.9,1.3,0.4,setosa
+5.1,3.5,1.4,0.3,setosa
+5.7,3.8,1.7,0.3,setosa
+5.1,3.8,1.5,0.3,setosa
+5.4,3.4,1.7,0.2,setosa
+5.1,3.7,1.5,0.4,setosa
+4.6,3.6,1.0,0.2,setosa
+5.1,3.3,1.7,0.5,setosa
+4.8,3.4,1.9,0.2,setosa
+5.0,3.0,1.6,0.2,setosa
+5.0,3.4,1.6,0.4,setosa
+5.2,3.5,1.5,0.2,setosa
+5.2,3.4,1.4,0.2,setosa
+4.7,3.2,1.6,0.2,setosa
+4.8,3.1,1.6,0.2,setosa
+5.4,3.4,1.5,0.4,setosa
+5.2,4.1,1.5,0.1,setosa
+5.5,4.2,1.4,0.2,setosa
+4.9,3.1,1.5,0.1,setosa
+5.0,3.2,1.2,0.2,setosa
+5.5,3.5,1.3,0.2,setosa
+4.9,3.1,1.5,0.1,setosa
+4.4,3.0,1.3,0.2,setosa
+5.1,3.4,1.5,0.2,setosa
+5.0,3.5,1.3,0.3,setosa
+4.5,2.3,1.3,0.3,setosa
+4.4,3.2,1.3,0.2,setosa
+5.0,3.5,1.6,0.6,setosa
+5.1,3.8,1.9,0.4,setosa
+4.8,3.0,1.4,0.3,setosa
+5.1,3.8,1.6,0.2,setosa
+4.6,3.2,1.4,0.2,setosa
+5.3,3.7,1.5,0.2,setosa
+5.0,3.3,1.4,0.2,setosa
+7.0,3.2,4.7,1.4,versicolor
+6.4,3.2,4.5,1.5,versicolor
+6.9,3.1,4.9,1.5,versicolor
+5.5,2.3,4.0,1.3,versicolor
+6.5,2.8,4.6,1.5,versicolor
+5.7,2.8,4.5,1.3,versicolor
+6.3,3.3,4.7,1.6,versicolor
+4.9,2.4,3.3,1.0,versicolor
+6.6,2.9,4.6,1.3,versicolor
+5.2,2.7,3.9,1.4,versicolor
+5.0,2.0,3.5,1.0,versicolor
+5.9,3.0,4.2,1.5,versicolor
+6.0,2.2,4.0,1.0,versicolor
+6.1,2.9,4.7,1.4,versicolor
+5.6,2.9,3.6,1.3,versicolor
+6.7,3.1,4.4,1.4,versicolor
+5.6,3.0,4.5,1.5,versicolor
+5.8,2.7,4.1,1.0,versicolor
+6.2,2.2,4.5,1.5,versicolor
+5.6,2.5,3.9,1.1,versicolor
+5.9,3.2,4.8,1.8,versicolor
+6.1,2.8,4.0,1.3,versicolor
+6.3,2.5,4.9,1.5,versicolor
+6.1,2.8,4.7,1.2,versicolor
+6.4,2.9,4.3,1.3,versicolor
+6.6,3.0,4.4,1.4,versicolor
+6.8,2.8,4.8,1.4,versicolor
+6.7,3.0,5.0,1.7,versicolor
+6.0,2.9,4.5,1.5,versicolor
+5.7,2.6,3.5,1.0,versicolor
+5.5,2.4,3.8,1.1,versicolor
+5.5,2.4,3.7,1.0,versicolor
+5.8,2.7,3.9,1.2,versicolor
+6.0,2.7,5.1,1.6,versicolor
+5.4,3.0,4.5,1.5,versicolor
+6.0,3.4,4.5,1.6,versicolor
+6.7,3.1,4.7,1.5,versicolor
+6.3,2.3,4.4,1.3,versicolor
+5.6,3.0,4.1,1.3,versicolor
+5.5,2.5,4.0,1.3,versicolor
+5.5,2.6,4.4,1.2,versicolor
+6.1,3.0,4.6,1.4,versicolor
+5.8,2.6,4.0,1.2,versicolor
+5.0,2.3,3.3,1.0,versicolor
+5.6,2.7,4.2,1.3,versicolor
+5.7,3.0,4.2,1.2,versicolor
+5.7,2.9,4.2,1.3,versicolor
+6.2,2.9,4.3,1.3,versicolor
+5.1,2.5,3.0,1.1,versicolor
+5.7,2.8,4.1,1.3,versicolor
+6.3,3.3,6.0,2.5,virginica
+5.8,2.7,5.1,1.9,virginica
+7.1,3.0,5.9,2.1,virginica
+6.3,2.9,5.6,1.8,virginica
+6.5,3.0,5.8,2.2,virginica
+7.6,3.0,6.6,2.1,virginica
+4.9,2.5,4.5,1.7,virginica
+7.3,2.9,6.3,1.8,virginica
+6.7,2.5,5.8,1.8,virginica
+7.2,3.6,6.1,2.5,virginica
+6.5,3.2,5.1,2.0,virginica
+6.4,2.7,5.3,1.9,virginica
+6.8,3.0,5.5,2.1,virginica
+5.7,2.5,5.0,2.0,virginica
+5.8,2.8,5.1,2.4,virginica
+6.4,3.2,5.3,2.3,virginica
+6.5,3.0,5.5,1.8,virginica
+7.7,3.8,6.7,2.2,virginica
+7.7,2.6,6.9,2.3,virginica
+6.0,2.2,5.0,1.5,virginica
+6.9,3.2,5.7,2.3,virginica
+5.6,2.8,4.9,2.0,virginica
+7.7,2.8,6.7,2.0,virginica
+6.3,2.7,4.9,1.8,virginica
+6.7,3.3,5.7,2.1,virginica
+7.2,3.2,6.0,1.8,virginica
+6.2,2.8,4.8,1.8,virginica
+6.1,3.0,4.9,1.8,virginica
+6.4,2.8,5.6,2.1,virginica
+7.2,3.0,5.8,1.6,virginica
+7.4,2.8,6.1,1.9,virginica
+7.9,3.8,6.4,2.0,virginica
+6.4,2.8,5.6,2.2,virginica
+6.3,2.8,5.1,1.5,virginica
+6.1,2.6,5.6,1.4,virginica
+7.7,3.0,6.1,2.3,virginica
+6.3,3.4,5.6,2.4,virginica
+6.4,3.1,5.5,1.8,virginica
+6.0,3.0,4.8,1.8,virginica
+6.9,3.1,5.4,2.1,virginica
+6.7,3.1,5.6,2.4,virginica
+6.9,3.1,5.1,2.3,virginica
+5.8,2.7,5.1,1.9,virginica
+6.8,3.2,5.9,2.3,virginica
+6.7,3.3,5.7,2.5,virginica
+6.7,3.0,5.2,2.3,virginica
+6.3,2.5,5.0,1.9,virginica
+6.5,3.0,5.2,2.0,virginica
+6.2,3.4,5.4,2.3,virginica
+5.9,3.0,5.1,1.8,virginica
+
diff --git a/packages/utilities/examples/extract-colors/extract-colors.ts b/packages/utilities/examples/extract-colors/extract-colors.ts
new file mode 100644
index 0000000..20a761a
--- /dev/null
+++ b/packages/utilities/examples/extract-colors/extract-colors.ts
@@ -0,0 +1,33 @@
+import {
+  createCanvas,
+  loadImage,
+} from "https://deno.land/x/canvas@v1.4.1/mod.ts";
+import { extractColors, Image } from "../../mod.ts";
+import { Color } from "https://deno.land/x/colors@v1.2.0/mod.ts";
+
+const image = await loadImage("utilities/examples/extract-colors/kagu.png");
+
+const canvas = createCanvas(image.width(), image.height());
+
+const ctx = canvas.getContext("2d");
+
+ctx.drawImage(image, 0, 0);
+
+const data = ctx.getImageData(0, 0, canvas.width, canvas.height);
+
+const img = new Image(data);
+
+const colors = extractColors(img, 32);
+
+const newCan = createCanvas(300, colors.length * 100);
+
+const newCtx = newCan.getContext("2d");
+
+colors.forEach((color, i) => {
+  newCtx.fillStyle = new Color(...color).toString();
+  newCtx.fillRect(0, i * 100, 300, 100);
+});
+Deno.writeFile(
+  "utilities/examples/extract-colors/out.png",
+  newCan.toBuffer("image/png"),
+);
diff --git a/packages/utilities/examples/extract-colors/kagu.png b/packages/utilities/examples/extract-colors/kagu.png
new file mode 100644
index 0000000..a5afca4
Binary files /dev/null and b/packages/utilities/examples/extract-colors/kagu.png differ
diff --git a/packages/utilities/examples/extract-colors/out.png b/packages/utilities/examples/extract-colors/out.png
new file mode 100644
index 0000000..34856ba
Binary files /dev/null and b/packages/utilities/examples/extract-colors/out.png differ
diff --git a/packages/utilities/examples/metrics/binary_iris.csv b/packages/utilities/examples/metrics/binary_iris.csv
new file mode 100644
index 0000000..01be6f2
--- /dev/null
+++ b/packages/utilities/examples/metrics/binary_iris.csv
@@ -0,0 +1,100 @@
+5.1,3.5,1.4,.2,"Setosa"
+4.9,3,1.4,.2,"Setosa"
+4.7,3.2,1.3,.2,"Setosa"
+4.6,3.1,1.5,.2,"Setosa"
+5,3.6,1.4,.2,"Setosa"
+5.4,3.9,1.7,.4,"Setosa"
+4.6,3.4,1.4,.3,"Setosa"
+5,3.4,1.5,.2,"Setosa"
+4.4,2.9,1.4,.2,"Setosa"
+4.9,3.1,1.5,.1,"Setosa"
+5.4,3.7,1.5,.2,"Setosa"
+4.8,3.4,1.6,.2,"Setosa"
+4.8,3,1.4,.1,"Setosa"
+4.3,3,1.1,.1,"Setosa"
+5.8,4,1.2,.2,"Setosa"
+5.7,4.4,1.5,.4,"Setosa"
+5.4,3.9,1.3,.4,"Setosa"
+5.1,3.5,1.4,.3,"Setosa"
+5.7,3.8,1.7,.3,"Setosa"
+5.1,3.8,1.5,.3,"Setosa"
+5.4,3.4,1.7,.2,"Setosa"
+5.1,3.7,1.5,.4,"Setosa"
+4.6,3.6,1,.2,"Setosa"
+5.1,3.3,1.7,.5,"Setosa"
+4.8,3.4,1.9,.2,"Setosa"
+5,3,1.6,.2,"Setosa"
+5,3.4,1.6,.4,"Setosa"
+5.2,3.5,1.5,.2,"Setosa"
+5.2,3.4,1.4,.2,"Setosa"
+4.7,3.2,1.6,.2,"Setosa"
+4.8,3.1,1.6,.2,"Setosa"
+5.4,3.4,1.5,.4,"Setosa"
+5.2,4.1,1.5,.1,"Setosa"
+5.5,4.2,1.4,.2,"Setosa"
+4.9,3.1,1.5,.2,"Setosa"
+5,3.2,1.2,.2,"Setosa"
+5.5,3.5,1.3,.2,"Setosa"
+4.9,3.6,1.4,.1,"Setosa"
+4.4,3,1.3,.2,"Setosa"
+5.1,3.4,1.5,.2,"Setosa"
+5,3.5,1.3,.3,"Setosa"
+4.5,2.3,1.3,.3,"Setosa"
+4.4,3.2,1.3,.2,"Setosa"
+5,3.5,1.6,.6,"Setosa"
+5.1,3.8,1.9,.4,"Setosa"
+4.8,3,1.4,.3,"Setosa"
+5.1,3.8,1.6,.2,"Setosa"
+4.6,3.2,1.4,.2,"Setosa"
+5.3,3.7,1.5,.2,"Setosa"
+5,3.3,1.4,.2,"Setosa"
+7,3.2,4.7,1.4,"Versicolor"
+6.4,3.2,4.5,1.5,"Versicolor"
+6.9,3.1,4.9,1.5,"Versicolor"
+5.5,2.3,4,1.3,"Versicolor"
+6.5,2.8,4.6,1.5,"Versicolor"
+5.7,2.8,4.5,1.3,"Versicolor"
+6.3,3.3,4.7,1.6,"Versicolor"
+4.9,2.4,3.3,1,"Versicolor"
+6.6,2.9,4.6,1.3,"Versicolor"
+5.2,2.7,3.9,1.4,"Versicolor"
+5,2,3.5,1,"Versicolor"
+5.9,3,4.2,1.5,"Versicolor"
+6,2.2,4,1,"Versicolor"
+6.1,2.9,4.7,1.4,"Versicolor"
+5.6,2.9,3.6,1.3,"Versicolor"
+6.7,3.1,4.4,1.4,"Versicolor"
+5.6,3,4.5,1.5,"Versicolor"
+5.8,2.7,4.1,1,"Versicolor"
+6.2,2.2,4.5,1.5,"Versicolor"
+5.6,2.5,3.9,1.1,"Versicolor"
+5.9,3.2,4.8,1.8,"Versicolor"
+6.1,2.8,4,1.3,"Versicolor"
+6.3,2.5,4.9,1.5,"Versicolor"
+6.1,2.8,4.7,1.2,"Versicolor"
+6.4,2.9,4.3,1.3,"Versicolor"
+6.6,3,4.4,1.4,"Versicolor"
+6.8,2.8,4.8,1.4,"Versicolor"
+6.7,3,5,1.7,"Versicolor"
+6,2.9,4.5,1.5,"Versicolor"
+5.7,2.6,3.5,1,"Versicolor"
+5.5,2.4,3.8,1.1,"Versicolor"
+5.5,2.4,3.7,1,"Versicolor"
+5.8,2.7,3.9,1.2,"Versicolor"
+6,2.7,5.1,1.6,"Versicolor"
+5.4,3,4.5,1.5,"Versicolor"
+6,3.4,4.5,1.6,"Versicolor"
+6.7,3.1,4.7,1.5,"Versicolor"
+6.3,2.3,4.4,1.3,"Versicolor"
+5.6,3,4.1,1.3,"Versicolor"
+5.5,2.5,4,1.3,"Versicolor"
+5.5,2.6,4.4,1.2,"Versicolor"
+6.1,3,4.6,1.4,"Versicolor"
+5.8,2.6,4,1.2,"Versicolor"
+5,2.3,3.3,1,"Versicolor"
+5.6,2.7,4.2,1.3,"Versicolor"
+5.7,3,4.2,1.2,"Versicolor"
+5.7,2.9,4.2,1.3,"Versicolor"
+6.2,2.9,4.3,1.3,"Versicolor"
+5.1,2.5,3,1.1,"Versicolor"
+5.7,2.8,4.1,1.3,"Versicolor"
\ No newline at end of file
diff --git a/packages/utilities/examples/metrics/metrics.ipynb b/packages/utilities/examples/metrics/metrics.ipynb
new file mode 100644
index 0000000..166967a
--- /dev/null
+++ b/packages/utilities/examples/metrics/metrics.ipynb
@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training time: 140.43580000003567ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "import {\n",
+    "  Cost,\n",
+    "  CPU,\n",
+    "  DenseLayer,\n",
+    "  Sequential,\n",
+    "  setupBackend,\n",
+    "  SigmoidLayer,\n",
+    "  tensor1D,\n",
+    "  tensor2D,\n",
+    "} from \"https://deno.land/x/netsaur@0.2.14/mod.ts\";\n",
+    "\n",
+    "import { parse } from \"jsr:@std/csv@1.0.3/parse\";\n",
+    "\n",
+    "// Import helpers for metrics\n",
+    "import {\n",
+    "  // Metrics\n",
+    "  ClassificationReport,\n",
+    "  // Split the dataset\n",
+    "  useSplit,\n",
+    "} from \"../../mod.ts\";\n",
+    "\n",
+    "// Define classes\n",
+    "const classes = [\"Setosa\", \"Versicolor\"];\n",
+    "\n",
+    "// Read the training dataset\n",
+    "const _data = Deno.readTextFileSync(\"binary_iris.csv\");\n",
+    "const data = parse(_data);\n",
+    "\n",
+    "// Get the predictors (x) and targets (y)\n",
+    "const x = data.map((fl) => fl.slice(0, 4).map(Number));\n",
+    "const y = data.map((fl) => classes.indexOf(fl[4]));\n",
+    "\n",
+    "// Split the dataset for training and testing\n",
+    "const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [\n",
+    "  [typeof x, typeof y],\n",
+    "  [typeof x, typeof y],\n",
+    "];\n",
+    "\n",
+    "// Setup the CPU backend for Netsaur\n",
+    "await setupBackend(CPU);\n",
+    "\n",
+    "// Create a sequential neural network\n",
+    "const net = new Sequential({\n",
+    "  // Set number of minibatches to 4\n",
+    "  // Set size of output to 4\n",
+    "  size: [4, 4],\n",
+    "\n",
+    "  // Disable logging during training\n",
+    "  silent: true,\n",
+    "\n",
+    "  // Define each layer of the network\n",
+    "  layers: [\n",
+    "    // A dense layer with 4 neurons\n",
+    "    DenseLayer({ size: [4] }),\n",
+    "    // A sigmoid activation layer\n",
+    "    SigmoidLayer(),\n",
+    "    // A dense layer with 1 neuron\n",
+    "    DenseLayer({ size: [1] }),\n",
+    "    // Another sigmoid layer\n",
+    "    SigmoidLayer(),\n",
+    "  ],\n",
+    "  // We are using MSE for finding cost\n",
+    "  cost: Cost.MSE,\n",
+    "});\n",
+    "\n",
+    "const time = performance.now();\n",
+    "\n",
+    "// Train the network\n",
+    "net.train(\n",
+    "  [\n",
+    "    {\n",
+    "      inputs: tensor2D(train[0]),\n",
+    "      outputs: tensor2D(train[1].map((x) => [x])),\n",
+    "    },\n",
+    "  ],\n",
+    "  // Train for 10000 epochs\n",
+    "  10000,\n",
+    ");\n",
+    "\n",
+    "console.log(`training time: ${performance.now() - time}ms`);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Classification Report</h3><br>Number of classes:\t2\n",
+       "<h3>Confusion Matrices</h3><br><br>Class: 0<table><thead><tr><th></th><th>P</th><th>N</th></tr><tr><td>P</td><td>16</td><td>0</td></tr><tr><td>N</td><td>0</td><td>14</td></table><br>Accuracy: 1<br>Precision: 1<br>Recall: 1<br>Specificity: 1<br>F1 Score: 1<br><br>Class: 1<table><thead><tr><th></th><th>P</th><th>N</th></tr><tr><td>P</td><td>14</td><td>0</td></tr><tr><td>N</td><td>0</td><td>16</td></table><br>Accuracy: 1<br>Precision: 1<br>Recall: 1<br>Specificity: 1<br>F1 Score: 1"
+      ],
+      "text/plain": [
+       "Classification Report\n",
+       "Number of classes:\t2\n",
+       "\n",
+       "==================\n",
+       "Confusion Matrices\n",
+       "==================\n",
+       "\n",
+       "Class: 0\n",
+       "\tP\tN\n",
+       "P\t16\t0\n",
+       "N\t0\t14\n",
+       "Accuracy: 1\n",
+       "Precision: 1\n",
+       "Recall: 1\n",
+       "Specificity: 1\n",
+       "F1 Score: 1\n",
+       "\n",
+       "Class: 1\n",
+       "\tP\tN\n",
+       "P\t14\t0\n",
+       "N\t0\t16\n",
+       "Accuracy: 1\n",
+       "Precision: 1\n",
+       "Recall: 1\n",
+       "Specificity: 1\n",
+       "F1 Score: 1\n"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "const res = await Promise.all(\n",
+    "  test[0].map((input) => net.predict(tensor1D(input))),\n",
+    ");\n",
+    "const y1 = res.map((x) => x.data[0] < 0.5 ? 0 : 1);\n",
+    "const cMatrix = new ClassificationReport(test[1], y1);\n",
+    "cMatrix;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Deno",
+   "language": "typescript",
+   "name": "deno"
+  },
+  "language_info": {
+   "file_extension": ".ts",
+   "mimetype": "text/x.typescript",
+   "name": "typescript",
+   "nb_converter": "script",
+   "pygments_lexer": "typescript",
+   "version": "5.3.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/packages/utilities/examples/metrics/metrics.ts b/packages/utilities/examples/metrics/metrics.ts
new file mode 100644
index 0000000..8c32548
--- /dev/null
+++ b/packages/utilities/examples/metrics/metrics.ts
@@ -0,0 +1,86 @@
+import {
+  Cost,
+  CPU,
+  DenseLayer,
+  Sequential,
+  setupBackend,
+  SigmoidLayer,
+  tensor2D,
+} from "https://deno.land/x/netsaur@0.3.0/mod.ts";
+
+import { parse } from "jsr:@std/csv@1.0.3/parse";
+
+// Import helpers for metrics
+import {
+  // Metrics
+  ClassificationReport,
+  // Split the dataset
+  useSplit,
+} from "../../mod.ts";
+
+// Define classes
+const classes = ["Setosa", "Versicolor"];
+
+// Read the training dataset
+const _data = Deno.readTextFileSync("examples/metrics/binary_iris.csv");
+const data = parse(_data);
+
+// Get the predictors (x) and targets (y)
+const x = data.map((fl) => fl.slice(0, 4).map(Number));
+const y = data.map((fl) => classes.indexOf(fl[4]));
+
+// Split the dataset for training and testing
+const [train, test] = useSplit({ ratio: [7, 3], shuffle: true }, x, y) as [
+  [typeof x, typeof y],
+  [typeof x, typeof y],
+];
+
+// Setup the CPU backend for Netsaur
+await setupBackend(CPU);
+
+// Create a sequential neural network
+const net = new Sequential({
+  // Set number of minibatches to 4
+  // Set size of output to 4
+  size: [4, 4],
+
+  // Disable logging during training
+  silent: true,
+
+  // Define each layer of the network
+  layers: [
+    // A dense layer with 4 neurons
+    DenseLayer({ size: [4] }),
+    // A sigmoid activation layer
+    SigmoidLayer(),
+    // A dense layer with 1 neuron
+    DenseLayer({ size: [1] }),
+    // Another sigmoid layer
+    SigmoidLayer(),
+  ],
+  // We are using MSE for finding cost
+  cost: Cost.MSE,
+});
+
+const time = performance.now();
+
+// Train the network
+net.train(
+  [
+    {
+      inputs: tensor2D(train[0]),
+      outputs: tensor2D(train[1].map((x) => [x])),
+    },
+  ],
+  // Train for 10000 epochs
+  10000,
+);
+
+console.log(`training time: ${performance.now() - time}ms`);
+
+const res = await net.predict(tensor2D(test[0]));
+const y1 = res.data.map(
+  (x) => (x < 0.5 ? 0 : 1),
+);
+const cMatrix = new ClassificationReport(test[1], y1);
+console.log("Confusion Matrix: ", cMatrix);
diff --git a/packages/utilities/mod.ts b/packages/utilities/mod.ts
new file mode 100644
index 0000000..8518201
--- /dev/null
+++ b/packages/utilities/mod.ts
@@ -0,0 +1,37 @@
+/**
+ * Machine Learning utilities for TypeScript.
+ *
+ * @example
+ * ```ts
+ * const data = [
+ *   "twinkle twinkle little star",
+ *   "How I wonder what you are",
+ *   "up above the world so high",
+ *   "like a diamond in the sky",
+ * ];
+ *
+ * // Clean the text
+ * const cleaner = new TextCleaner({
+ *   lowercase: true,
+ *   stripHtml: true,
+ *   stripNewlines: true,
+ *   normalizeWhiteSpaces: true,
+ * });
+ * x = cleaner.clean(x);
+ *
+ * // Tokenize the text
+ * const tokenizer = new SplitTokenizer();
+ * tokenizer.fit(x);
+ * const x_tokens = tokenizer.transform(x);
+ *
+ * // Vectorize the tokens
+ * const vectorizer = new CountVectorizer(tokenizer.vocabulary.size);
+ * const x_vec = vectorizer.transform(x_tokens, "f32");
+ *
+ * // Apply Tf-Idf transformation
+ * const transformer = new TfIdfTransformer();
+ * console.log(transformer.fit(x_vec).transform(x_vec));
+ * ```
+ * @module
+ */
+export * from "./src/mod.ts";
diff --git a/packages/utilities/src/constants/stop_words.ts b/packages/utilities/src/constants/stop_words.ts
new file mode 100644
index 0000000..d5ac7b4
--- /dev/null
+++ b/packages/utilities/src/constants/stop_words.ts
@@ -0,0 +1,182 @@
+/** List obtained from NLTK */
+export const DefaultIgnoreList = [
+  "i",
+  "me",
+  "my",
+  "myself",
+  "we",
+  "our",
+  "ours",
+  "ourselves",
+  "you",
+  "you're",
+  "you've",
+  "you'll",
+  "you'd",
+  "your",
+  "yours",
+  "yourself",
+  "yourselves",
+  "he",
+  "him",
+  "his",
+  "himself",
+  "she",
+  "she's",
+  "her",
+  "hers",
+  "herself",
+  "it",
+  "it's",
+  "its",
+  "itself",
+  "they",
+  "them",
+  "their",
+  "theirs",
+  "themselves",
+  "what",
+  "which",
+  "who",
+  "whom",
+  "this",
+  "that",
+  "that'll",
+  "these",
+  "those",
+  "am",
+  "is",
+  "are",
+  "was",
+  "were",
+  "be",
+  "been",
+  "being",
+  "have",
+  "has",
+  "had",
+  "having",
+  "do",
+  "does",
+  "did",
+  "doing",
+  "a",
+  "an",
+  "the",
+  "and",
+  "but",
+  "if",
+  "or",
+  "because",
+  "as",
+  "until",
+  "while",
+  "of",
+  "at",
+  "by",
+  "for",
+  "with",
+  "about",
+  "against",
+  "between",
+  "into",
+  "through",
+  "during",
+  "before",
+  "after",
+  "above",
+  "below",
+  "to",
+  "from",
+  "up",
+  "down",
+  "in",
+  "out",
+  "on",
+  "off",
+  "over",
+  "under",
+  "again",
+  "further",
+  "then",
+  "once",
+  "here",
+  "there",
+  "when",
+  "where",
+  "why",
+  "how",
+  "all",
+  "any",
+  "both",
+  "each",
+  "few",
+  "more",
+  "most",
+  "other",
+  "some",
+  "such",
+  "no",
+  "nor",
+  "not",
+  "only",
+  "own",
+  "same",
+  "so",
+  "than",
+  "too",
+  "very",
+  "s",
+  "t",
+  "can",
+  "will",
+  "just",
+  "don",
+  "don't",
+  "should",
+  "should've",
+  "now",
+  "d",
+  "ll",
+  "m",
+  "o",
+  "re",
+  "ve",
+  "y",
+  "ain",
+  "aren",
+  "aren't",
+  "couldn",
+  "couldn't",
+  "didn",
+  "didn't",
+  "doesn",
+  "doesn't",
+  "hadn",
+  "hadn't",
+  "hasn",
+  "hasn't",
+  "haven",
+  "haven't",
+  "isn",
+  "isn't",
+  "ma",
+  "mightn",
+  "mightn't",
+  "mustn",
+  "mustn't",
+  "needn",
+  "needn't",
+  "shan",
+  "shan't",
+  "shouldn",
+  "shouldn't",
+  "wasn",
+  "wasn't",
+  "weren",
+  "weren't",
+  "won",
+  "won't",
+  "wouldn",
+  "wouldn't",
+];
diff --git a/packages/utilities/src/encoding/categorical.ts b/packages/utilities/src/encoding/categorical.ts
new file mode 100644
index 0000000..a7d5700
--- /dev/null
+++ b/packages/utilities/src/encoding/categorical.ts
@@ -0,0 +1,92 @@
+import { Matrix, type MatrixLike } from "../mod.ts";
+import type { DataType, DType, DTypeValue } from "../utils/common_types.ts";
+
+/** Convert class labels into categorical variables (one-hot) */
+export class CategoricalEncoder<T> {
+  /** Map categories to indices */
+  mapping: Map<T, number>;
+  /** An internal counter for remembering the last index in mapping. */
+  #lastToken: Uint32Array;
+  constructor() {
+    this.mapping = new Map();
+    this.#lastToken = new Uint32Array(1);
+  }
+  /** Construct a mapping from a given set of text. */
+  fit(targets: T[]): this {
+    let i = 0;
+    while (i < targets.length) {
+      if (!this.mapping.has(targets[i])) {
+        const token = this.#incrementToken();
+        this.mapping.set(targets[i], token);
+      }
+      i += 1;
+    }
+    return this;
+  }
+  /** One-hot encoding of categorical values */
+  transform<DT extends DataType>(targets: T[], dType: DT): Matrix<DT> {
+    const res = new Matrix<DT>(dType, [targets.length, this.#lastToken[0]]);
+    let i = 0;
+    while (i < targets.length) {
+      const index = this.mapping.get(targets[i]);
+      if (index !== 0 && !index) {
+        i += 1;
+        continue;
+      }
+      res.setCell(i, index, 1);
+      i += 1;
+    }
+    return res;
+  }
+  untransform<DT extends DataType>(data: MatrixLike<DT>): T[] {
+    const matrix = new Matrix(data);
+    const res = new Array(matrix.nRows);
+    for (let i = 0; i < res.length; i += 1) {
+      const idx = matrix.row(i).findIndex((x) => x === 1);
+      res[i] = this.getOg(idx) || "__unknown__";
+    }
+    return res;
+  }
+  getOg(data: number): T | undefined {
+    for (const [k, v] of this.mapping.entries()) {
+      if (v === data) {
+        return k;
+      }
+    }
+    return undefined;
+  }
+  #incrementToken(): number {
+    return Atomics.add(this.#lastToken, 0, 1);
+  }
+  /**
+   * Convert softmax outputs into categorical outputs
+   * This method mutates the original matrix.
+   * @returns The modified matrix.
+   */
+  static fromSoftmax<DT extends DataType>(data: MatrixLike<DT>): Matrix<DT> {
+    const matrix = new Matrix(data);
+    for (let i = 0; i < matrix.nRows; i += 1) {
+      const max = matrix
+        .row(i)
+        // @ts-ignore It can reduce.
+        .reduce(
+          (acc: number, curr: DTypeValue<DT>, i: number, arr: DType<DT>) =>
+            arr[acc] > curr ? acc : i,
+          0,
+        );
+      if (
+        data.data instanceof BigInt64Array ||
+        data.data instanceof BigUint64Array
+      ) {
+        const newR = new Array(matrix.nCols).fill(0n);
+        newR[max] = 1n;
+        matrix.setRow(i, newR);
+      } else {
+        const newR = new Array(matrix.nCols).fill(0);
+        newR[max] = 1;
+        matrix.setRow(i, newR);
+      }
+    }
+    return matrix;
+  }
+}
diff --git a/packages/utilities/src/encoding/mod.ts b/packages/utilities/src/encoding/mod.ts
new file mode 100644
index 0000000..26408b2
--- /dev/null
+++ b/packages/utilities/src/encoding/mod.ts
@@ -0,0 +1,6 @@
+/**
+ * Encoding data into different representations.
+ * @module
+ */
+
+export * from "./categorical.ts";
diff --git a/packages/utilities/src/image/colors/common.ts b/packages/utilities/src/image/colors/common.ts
new file mode 100644
index 0000000..7bb0ecf
--- /dev/null
+++ b/packages/utilities/src/image/colors/common.ts
@@ -0,0 +1,90 @@
+import type { Image } from "../../utils/mod.ts";
+import type { Pixel } from "../../utils/common_types.ts";
+import type { ColorHistogram } from "./histogram.ts";
+
+export function getAverageColor(
+  vbox: ColorRange,
+  histo: ColorHistogram,
+): Pixel {
+  let total = 0;
+  let totalR = 0, totalG = 0, totalB = 0;
+  let ri = vbox.r.min;
+  while (ri <= vbox.r.max) {
+    let gi = vbox.g.min;
+    while (gi <= vbox.g.max) {
+      let bi = vbox.b.min;
+      while (bi <= vbox.b.max) {
+        const count = histo.getQuantized([ri, gi, bi]) || 0;
+        total += count;
+        totalR += count * (ri + 0.5) * 8;
+        totalG += count * (gi + 0.5) * 8;
+        totalB += count * (bi + 0.5) * 8;
+        bi += 1;
+      }
+      gi += 1;
+    }
+    ri += 1;
+  }
+  if (total) {
+    return [
+      ~~(totalR / total),
+      ~~(totalG / total),
+      ~~(totalB / total),
+      255,
+    ];
+  }
+  // In case box is empty
+  return [
+    Math.trunc(8 * (vbox.r.min + vbox.r.max + 1) / 2),
+    Math.trunc(8 * (vbox.g.min + vbox.g.max + 1) / 2),
+    Math.trunc(8 * (vbox.b.min + vbox.b.max + 1) / 2),
+    255,
+  ];
+}
+
+/** The vbox */
+export interface ColorRange {
+  r: { min: number; max: number };
+  g: { min: number; max: number };
+  b: { min: number; max: number };
+}
+
+/** Get the minimum and maximum RGB values. */
+export function getColorRange(
+  image: Image,
+  sigBits = 5,
+): ColorRange {
+  const quantizeBy = 8 - sigBits;
+  const range = {
+    r: { min: 1000, max: 0 },
+    g: { min: 1000, max: 0 },
+    b: { min: 1000, max: 0 },
+  };
+  let i = 0;
+  while (i < image.pixels) {
+    const pixel = image.getNthPixel(i).map((x) => x ? x >> quantizeBy : 0);
+    if (pixel[0] < range.r.min) {
+      range.r.min = pixel[0];
+    }
+    if (pixel[0] > range.r.max) {
+      range.r.max = pixel[0];
+    }
+
+    if (pixel[1] < range.g.min) {
+      range.g.min = pixel[1];
+    }
+    if (pixel[1] > range.g.max) {
+      range.g.max = pixel[1];
+    }
+
+    if (pixel[2] < range.b.min) {
+      range.b.min = pixel[2];
+    }
+    if (pixel[2] > range.b.max) {
+      range.b.max = pixel[2];
+    }
+
+    i += 1;
+  }
+  return range;
+}
diff --git a/packages/utilities/src/image/colors/histogram.ts b/packages/utilities/src/image/colors/histogram.ts
new file mode 100644
index 0000000..2f94dd9
--- /dev/null
+++ b/packages/utilities/src/image/colors/histogram.ts
@@ -0,0 +1,61 @@
+import type { Image } from "../../utils/mod.ts";
+import type { Pixel } from "../../utils/common_types.ts";
+
+/**
+ * Histogram of colors with reduced space
+ * Effectively quantizes the image into 32768 colors
+ */
+export class ColorHistogram {
+  #data: Uint32Array;
+  #quantizeBy: number;
+  sigBits: number;
+  constructor(sigBits: number) {
+    this.sigBits = sigBits;
+    this.#quantizeBy = 8 - sigBits;
+    this.#data = new Uint32Array(1 << (sigBits * 3));
+  }
+  #getIndex([r, g, b]: [number, number, number, number?]) {
+    // ignore alpha
+    const index = ((r >> this.#quantizeBy) << (this.sigBits << 1)) +
+      ((g >> this.#quantizeBy) << this.sigBits) +
+      (b >> this.#quantizeBy);
+    return index;
+  }
+  get(color: Pixel): number {
+    const index = this.#getIndex(color);
+    return this.#data[index];
+  }
+  getQuantized(color: Pixel): number {
+    const index = (color[0] << 10) + (color[1] << 5) + color[2];
+    return this.#data[index];
+  }
+  add(color: Pixel, amount: number): number {
+    const index = this.#getIndex(color);
+    return Atomics.add(this.#data, index, amount);
+  }
+  get raw(): Uint32Array {
+    return this.#data;
+  }
+  get length(): number {
+    return this.#data.filter((x) => x).length;
+  }
+  static getColor(index: number, sigBits: number): Pixel {
+    const quantizeBy = 8 - sigBits;
+    const ri = index >> 10;
+    const gi = (index - (ri << 10)) >> 5;
+    const bi = index - (ri << 10) - (gi << 5);
+    return [ri << quantizeBy, gi << quantizeBy, bi << quantizeBy, 255];
+  }
+}
+
+/** Get a histogram of frequency of colors. */
+export function getHistogram(image: Image, sigBits = 5): ColorHistogram {
+  const histo = new ColorHistogram(sigBits);
+  let i = 0;
+  while (i < image.pixels) {
+    const hIndex = image.getNthPixel(i);
+    histo.add(hIndex, 1);
+    i += 1;
+  }
+  return histo;
+}
diff --git a/packages/utilities/src/image/colors/median_cut.ts b/packages/utilities/src/image/colors/median_cut.ts
new file mode 100644
index 0000000..3dd32e3
--- /dev/null
+++ b/packages/utilities/src/image/colors/median_cut.ts
@@ -0,0 +1,282 @@
+import { type ColorHistogram, getHistogram } from "./histogram.ts";
+import { getAverageColor, getColorRange } from "./common.ts";
+import type { ColorRange } from "./common.ts";
+import type { Pixel } from "../../utils/common_types.ts";
+import type { Image } from "../../utils/mod.ts";
+
+/// Uses Modified Median Cut Algorithm
+/// TypeScript port of Leptonica
+/// http://www.leptonica.org/
+
+export function quantizeByMedianCut(
+  image: Image,
+  extractCount: number,
+  sigBits = 5,
+): Pixel[] {
+  if (sigBits <= 4) console.warn("Setting sigBits less than 5 may not work.");
+  const vbox = getColorRange(image, sigBits);
+  const histo = getHistogram(image, sigBits);
+  return quantize(vbox, histo, extractCount);
+}
+
+function quantize(
+  vbox: ColorRange,
+  histo: ColorHistogram,
+  extractCount: number,
+): Pixel[] {
+  const vboxes: ColorRange[] = [vbox];
+
+  // Avoid an infinite loop
+  const maxIter = 1000;
+  let i = 0;
+
+  const firstExtractCount = ~~(extractCount >> 1);
+  let generated = 1;
+
+  while (i < maxIter) {
+    const lastBox = vboxes.shift();
+    if (!lastBox) break; // This shouldn't happen
+    if (!vboxSize(lastBox, histo)) {
+      vboxes.push(lastBox);
+      i += 1;
+      continue;
+    }
+    const cut = medianCutApply(lastBox, histo);
+    if (cut) {
+      vboxes.push(cut[0], cut[1]);
+      generated += 1;
+    } else vboxes.push(lastBox);
+    if (generated >= firstExtractCount) break;
+    i += 1;
+  }
+
+  vboxes.sort((a, b) =>
+    (vboxSize(b, histo) * vboxVolume(b)) - (vboxSize(a, histo) * vboxVolume(a))
+  );
+  const secondExtractCount = extractCount - vboxes.length;
+  i = 0;
+  generated = 0;
+
+  while (i < maxIter) {
+    const lastBox = vboxes.shift();
+    if (!lastBox) break; // This shouldn't happen
+    if (!vboxSize(lastBox, histo)) {
+      vboxes.push(lastBox);
+      i += 1;
+      continue;
+    }
+    const cut = medianCutApply(lastBox, histo);
+
+    if (cut) {
+      vboxes.push(cut[0], cut[1]);
+      generated += 1;
+    } else vboxes.push(lastBox);
+    if (generated >= secondExtractCount) break;
+    i += 1;
+  }
+  vboxes.sort((a, b) => vboxSize(b, histo) - vboxSize(a, histo));
+  return vboxes.map((x) => getAverageColor(x, histo)).slice(0, extractCount);
+}
+
+/** Get number of colors in vbox */
+function vboxSize(vbox: ColorRange, histo: ColorHistogram): number {
+  let count = 0;
+  let ri = vbox.r.min;
+  while (ri <= vbox.r.max) {
+    let gi = vbox.g.min;
+    while (gi <= vbox.g.max) {
+      let bi = vbox.b.min;
+      while (bi <= vbox.b.max) {
+        count += histo.get([ri, gi, bi, 255]) || 0;
+        bi += 1;
+      }
+      gi += 1;
+    }
+    ri += 1;
+  }
+  return count;
+}
+
+/** Get volume by dimensions of vbox */
+function vboxVolume(vbox: ColorRange): number {
+  return ~~(vbox.r.max - vbox.r.min) * ~~(vbox.g.max - vbox.g.min) *
+    ~~(vbox.b.max - vbox.b.min);
+}
+
+/** Cut vbox into two */
+function medianCutApply(
+  vbox: ColorRange,
+  histo: ColorHistogram,
+): [ColorRange, ColorRange] | false {
+  const count = vboxSize(vbox, histo);
+
+  if (!count || count === 1) return false;
+  const rw = vbox.r.max - vbox.r.min + 1;
+  const gw = vbox.g.max - vbox.g.min + 1;
+  const bw = vbox.b.max - vbox.b.min + 1;
+
+  const axis = Math.max(rw, gw, bw);
+
+  // Find partial sums along each axis
+  const sumAlongAxis = [];
+  // avoid running another loop to compute sum
+  let totalSum = 0;
+  switch (axis) {
+    case rw: {
+      let i = vbox.r.min;
+      while (i <= vbox.r.max) {
+        let tempSum = 0;
+        let j = vbox.g.min;
+        while (j < vbox.g.max) {
+          let k = vbox.b.min;
+          while (k < vbox.b.max) {
+            tempSum += histo.getQuantized([i, j, k]) || 0;
+            k += 1;
+          }
+          j += 1;
+        }
+        totalSum += tempSum;
+        sumAlongAxis[i] = totalSum;
+        i += 1;
+      }
+      break;
+    }
+    case gw: {
+      let i = vbox.g.min;
+      while (i <= vbox.g.max) {
+        let tempSum = 0;
+        let j = vbox.r.min;
+        while (j < vbox.r.max) {
+          let k = vbox.b.min;
+          while (k < vbox.b.max) {
+            tempSum += histo.getQuantized([j, i, k]) || 0;
+            k += 1;
+          }
+          j += 1;
+        }
+        totalSum += tempSum;
+        sumAlongAxis[i] = totalSum;
+        i += 1;
+      }
+      break;
+    }
+    default: {
+      let i = vbox.b.min;
+      while (i <= vbox.b.max) {
+        let tempSum = 0;
+        let j = vbox.r.min;
+        while (j < vbox.r.max) {
+          let k = vbox.g.min;
+          while (k < vbox.g.max) {
+            tempSum += histo.getQuantized([j, k, i]) || 0;
+            k += 1;
+          }
+          j += 1;
+        }
+        totalSum += tempSum;
+        sumAlongAxis[i] = totalSum;
+        i += 1;
+      }
+      break;
+    }
+  }
+  // Apply median cut
+  switch (axis) {
+    case rw: {
+      let i = vbox.r.min;
+      while (i <= vbox.r.max) {
+        // Find the mid point through linear search
+        if (sumAlongAxis[i] < totalSum / 2) {
+          let cutAt = 0;
+          const vbox1 = {
+            r: { min: vbox.r.min, max: vbox.r.max },
+            g: { min: vbox.g.min, max: vbox.g.max },
+            b: { min: vbox.b.min, max: vbox.b.max },
+          };
+          const vbox2 = {
+            r: { min: vbox.r.min, max: vbox.r.max },
+            g: { min: vbox.g.min, max: vbox.g.max },
+            b: { min: vbox.b.min, max: vbox.b.max },
+          };
+          const left = i - vbox.r.min;
+          const right = vbox.r.max - i;
+          if (left <= right) {
+            cutAt = Math.min(vbox.r.max - 1, Math.trunc(i + right / 2));
+          } else cutAt = Math.max(vbox.r.min, Math.trunc(i - 1 - left / 2));
+
+          while (!sumAlongAxis[cutAt]) cutAt += 1;
+
+          vbox1.r.max = cutAt;
+          vbox2.r.min = cutAt + 1;
+          return [vbox1, vbox2];
+        }
+        i += 1;
+      }
+      break;
+    }
+    case gw: {
+      let i = vbox.g.min;
+      while (i <= vbox.g.max) {
+        // Find the mid point through linear search
+        if (sumAlongAxis[i] < totalSum / 2) {
+          let cutAt = 0;
+          const vbox1 = {
+            r: { min: vbox.r.min, max: vbox.r.max },
+            g: { min: vbox.g.min, max: vbox.g.max },
+            b: { min: vbox.b.min, max: vbox.b.max },
+          };
+          const vbox2 = {
+            r: { min: vbox.r.min, max: vbox.r.max },
+            g: { min: vbox.g.min, max: vbox.g.max },
+            b: { min: vbox.b.min, max: vbox.b.max },
+          };
+          const left = i - vbox.g.min;
+          const right = vbox.g.max - i;
+          if (left <= right) {
+            cutAt = Math.min(vbox.g.max - 1, Math.trunc(i + right / 2));
+          } else cutAt = Math.max(vbox.g.min, Math.trunc(i - 1 - left / 2));
+          while (!sumAlongAxis[cutAt]) cutAt += 1;
+
+          vbox1.g.max = cutAt;
+          vbox2.g.min = cutAt + 1;
+          return [vbox1, vbox2];
+        }
+        i += 1;
+      }
+      break;
+    }
+    default: {
+      let i = vbox.b.min;
+      while (i <= vbox.b.max) {
+        // Find the mid point through linear search
+        if (sumAlongAxis[i] < totalSum / 2) {
+          let cutAt = 0;
+          const vbox1 = {
+            r: { min: vbox.r.min, max: vbox.r.max },
+            g: { min: vbox.g.min, max: vbox.g.max },
+            b: { min: vbox.b.min, max: vbox.b.max },
+          };
+          const vbox2 = {
+            r: { min: vbox.r.min, max: vbox.r.max },
+            g: { min: vbox.g.min, max: vbox.g.max },
+            b: { min: vbox.b.min, max: vbox.b.max },
+          };
+          const left = i - vbox.b.min;
+          const right = vbox.b.max - i;
+          if (left <= right) {
+            cutAt = Math.min(vbox.b.max - 1, Math.trunc(i + right / 2));
+          } else cutAt = Math.max(vbox.b.min, Math.trunc(i - 1 - left / 2));
+          while (!sumAlongAxis[cutAt]) cutAt += 1;
+
+          vbox1.b.max = cutAt;
+          vbox2.b.min = cutAt + 1;
+          return [vbox1, vbox2];
+        }
+        i += 1;
+      }
+      break;
+    }
+  }
+
+  return false;
+}
diff --git a/packages/utilities/src/image/colors/mod.ts b/packages/utilities/src/image/colors/mod.ts
new file mode 100644
index 0000000..91acca6
--- /dev/null
+++ b/packages/utilities/src/image/colors/mod.ts
@@ -0,0 +1,16 @@
+/**
+ * Image-related utilities for machine learning.
+ * @module
+ */
+
+import { Image } from "../../utils/mod.ts";
+import type { Pixel } from "../../utils/common_types.ts";
+import { quantizeByMedianCut } from "./median_cut.ts";
+
+/** Extract colors from an image. */
+export function extractColors(image: Image, nColors: number): Pixel[] {
+  return quantizeByMedianCut(image, nColors, 5);
+}
+
+export { getHistogram } from "./histogram.ts";
+export { Image };
diff --git a/packages/utilities/src/image/mod.ts b/packages/utilities/src/image/mod.ts
new file mode 100644
index 0000000..5c71f8b
--- /dev/null
+++ b/packages/utilities/src/image/mod.ts
@@ -0,0 +1,7 @@
+/**
+ * Image-related utilities for Machine Learning
+ * and Data Science.
+ * @module
+ */
+export * from "./colors/mod.ts";
+export * from "./patches/patch_2d.ts";
diff --git a/packages/utilities/src/image/patches/patch_2d.ts b/packages/utilities/src/image/patches/patch_2d.ts
new file mode 100644
index 0000000..d48bd2f
--- /dev/null
+++ b/packages/utilities/src/image/patches/patch_2d.ts
@@ -0,0 +1,81 @@
+import type { Image } from "../../utils/mod.ts";
+import type { Patch2d, PatchCollection } from "../../utils/common_types.ts";
+
+/**
+ * Extract patches from a 2d image.
+ */
+
+/**
+ * Get the row and column of the nth element of a
+ * 2d array.
+ * @param n Current Position
+ * @param width Width of a row
+ * @returns [row, column]
+ */
+function clamp(n: number, width: number): [number, number] {
+  if (n >= width) return [~~(n / width), n % width];
+  return [0, n];
+}
+
+/** Private function to extract patches */
+function extract(image: Image, options: Patch2d): [Uint8ClampedArray, number] {
+  /** Get number of possible patches in each dimension */
+  const nX = image.width - options.width + 1;
+  const nY = image.height - options.height + 1;
+  /** Total number of patches is nX * nY */
+  const nPatches = nX * nY;
+  /** Area of each patch, used to calculate offset of resulting array */
+  const patchArea = options.width * options.height;
+
+  const res = new Uint8ClampedArray(
+    options.width * options.height * image.channels * nPatches,
+  );
+
+  let i = 0;
+  while (i < nPatches) {
+    const [row, col] = clamp(i, nX);
+    /** Starting index of the current patch */
+    const offset = row * image.width + col;
+    let j = 0;
+    while (j < options.height) {
+      /** Starting index of the current subrow */
+      const patchRow = j * image.width;
+      /** Copy the entire patchArea-size patch into the resulting array */
+      res.set(
+        image.data.slice(
+          (offset + patchRow) * image.channels,
+          (offset + options.width + patchRow) * image.channels,
+        ),
+        (i * patchArea + j * options.width) * image.channels,
+      );
+      j += 1;
+    }
+    i += 1;
+  }
+  return [res, nPatches];
+}
+
+/**
+ * Extract patches from a 2d image
+ * @param image Source image to extract patches from
+ * @param options Dimensions of a single patch
+ * @returns A collection of patches as a single Uint8ClampedArray
+ */
+export function patch2d(image: Image, options: Patch2d): PatchCollection {
+  if (image.width < options.width) {
+    throw new Error("Patch width cannot be greater than image width.");
+  }
+  if (image.height < options.height) {
+    throw new Error("Patch height cannot be greater than image width.");
+  }
+
+  const [patches, n] = extract(image, options);
+
+  return {
+    width: options.width,
+    height: options.height,
+    channels: image.channels,
+    size: n,
+    data: patches,
+  };
+}
diff --git a/packages/utilities/src/metrics/classification.ts b/packages/utilities/src/metrics/classification.ts
new file mode 100644
index 0000000..07f3786
--- /dev/null
+++ b/packages/utilities/src/metrics/classification.ts
@@ -0,0 +1,223 @@
+import { useUnique } from "../utils/mod.ts";
+
+interface Report {
+  c: ConfusionMatrix;
+  precision: number;
+  recall: number;
+  f1: number;
+  support: number;
+}
+
+/** A report with metrics for classification results */
+export class ClassificationReport {
+  /** Number of elements classified correctly */
+  true: number;
+  /** Number of elements classified incorrectly */
+  false: number;
+  /** Total number of elements */
+  size: number;
+  labels: string[];
+  reports: Map<string, Report>;
+  constructor(y: ArrayLike<unknown>, y1: ArrayLike<unknown>) {
+    const unique = useUnique(y);
+    if (unique.length <= 1) {
+      throw new Error(
+        `Cannot create a classification report for less than 1 class.`,
+      );
+    }
+    this.true = 0;
+    this.false = 0;
+    this.size = y.length;
+    this.labels = unique.map((x) => `${x}`);
+    this.reports = new Map();
+    for (const label of unique) {
+      let [tp, fn, fp, tn] = [0, 0, 0, 0];
+      for (let i = 0; i < y.length; i += 1) {
+        if (y1[i] !== label && y[i] !== label) tn += 1;
+        else if (y1[i] !== label && y[i] === unique[0]) fn += 1;
+        else if (y1[i] === label && y[i] !== label) fp += 1;
+        else tp += 1;
+      }
+      this.true += tp + tn;
+      this.false += fp + fn;
+      const cMatrix = new ConfusionMatrix([tp, fn, fp, tn]);
+      this.reports.set(`${label}`, {
+        c: cMatrix,
+        precision: precisionScore(cMatrix),
+        f1: f1Score(cMatrix),
+        recall: recallScore(cMatrix),
+        support: cMatrix.truePositive + cMatrix.falseNegative,
+      });
+    }
+  }
+  toString(): string {
+    let res = `Classification Report`;
+    res += `\nNumber of classes:\t${this.labels.length}\n`;
+    res += `Class\tPreci\tF1\tRec\tSup`;
+    for (const [label, report] of this.reports.entries()) {
+      res += `\n${label}`;
+      res +=
+        `\t${report.precision}\t${report.f1}\t${report.recall}\t${report.support}`;
+    }
+    res += `\nAccuracy\t\t${
+      this.true / (this.true + this.false)
+    }\t${this.size}`;
+    return res;
+  }
+  toHtml(): string {
+    let res =
+      `<table><thead><tr><th>Class</th><th>Precision</th><th>F1Score</th><th>Recall</th><th>Support</th></tr></thead>`;
+    for (const [label, report] of this.reports.entries()) {
+      res +=
+        `<tr><td>${label}</td><td>${report.precision}</td><td>${report.f1}</td><td>${report.recall}</td><td>${report.support}</td></tr>`;
+    }
+    res += `<tr><td>Accuracy</td><td></td><td></td><td>${
+      this.true / (this.true + this.false)
+    }</td><td>${this.size}</td></tr>`;
+    res += `</table>`;
+    return res;
+  }
+  [Symbol.for("Deno.customInspect")](): string {
+    return this.toString();
+  }
+  [Symbol.for("Jupyter.display")](): Record<string, string> {
+    return {
+      // Plain text content
+      "text/plain": this.toString(),
+
+      // HTML output
+      "text/html": this.toHtml(),
+    };
+  }
+  toJson(): {
+    class: string[];
+    precision: number[];
+    f1: number[];
+    recall: number[];
+    support: number[];
+  } {
+    const reports = Array.from(this.reports.entries());
+    return {
+      class: reports.map((x) => x[0]),
+      precision: reports.map((x) => x[1].precision),
+      f1: reports.map((x) => x[1].f1),
+      recall: reports.map((x) => x[1].recall),
+      support: reports.map((x) => x[1].support),
+    };
+  }
+  /*
+  [Symbol.for("Deno.customInspect")]() {
+    let res = `\t${this.labels.join("\t")}`
+    for (const label of this.labels) {
+      res += `\n${label}`
+      for (const label1 of this.labels) {
+        res += `\t${}`
+      }
+    }
+    return `\n\t${this.labelP}\t${this.labelN}\n${this.labelP}\t${this.truePositive}\t${this.falseNegative}\n${this.labelN}\t${this.falsePositive}\t${this.trueNegative}`;
+  }
+  */
+}
+
+/** Confusion matrix for the result. */
+export class ConfusionMatrix {
+  /** Number of positive elements classified correctly */
+  truePositive: number;
+  /** Number of negative elements classified incorrectly */
+  falsePositive: number;
+  /** Number of negative elements classified correctly */
+  trueNegative: number;
+  /** Number of positive elements classified incorrectly */
+  falseNegative: number;
+  /** Number of elements classified correctly */
+  true: number;
+  /** Number of elements classified incorrectly */
+  false: number;
+  /** Total number of elements */
+  size: number;
+  /** Label for positive elements */
+  labelP: string;
+  /** Label for negative elements */
+  labelN: string;
+  constructor(
+    [tp, fn, fp, tn]: [number, number, number, number],
+    [label1, label2]: [string?, string?] = [],
+  ) {
+    this.truePositive = tp;
+    this.falseNegative = fn;
+    this.falsePositive = fp;
+    this.trueNegative = tn;
+    this.true = tn + tp;
+    this.false = fn + fp;
+    this.size = this.true + this.false;
+    this.labelP = label1 || "P";
+    this.labelN = label2 || "N";
+  }
+  valueOf(): [number, number, number, number] {
+    return [
+      this.truePositive,
+      this.falseNegative,
+      this.falsePositive,
+      this.trueNegative,
+    ];
+  }
+  [Symbol.for("Deno.customInspect")](): string {
+    return `\n\t${this.labelP}\t${this.labelN}\n${this.labelP}\t${this.truePositive}\t${this.falseNegative}\n${this.labelN}\t${this.falsePositive}\t${this.trueNegative}`;
+  }
+  static fromResults(
+    y: ArrayLike<unknown>,
+    y1: ArrayLike<unknown>,
+  ): ConfusionMatrix {
+    const unique = useUnique(y);
+    if (unique.length !== 2) {
+      throw new Error(
+        `Cannot create confusion matrix for ${unique.length} classes. Try ClassificationReport instead.`,
+      );
+    }
+    let [tp, fn, fp, tn] = [0, 0, 0, 0];
+    for (let i = 0; i < y.length; i += 1) {
+      if (y1[i] === unique[1] && y[i] === unique[1]) tn += 1;
+      else if (y1[i] === unique[1] && y[i] === unique[0]) fn += 1;
+      else if (y1[i] === unique[0] && y[i] === unique[1]) fp += 1;
+      else tp += 1;
+    }
+    return new this([tp, fn, fp, tn], [`${unique[0]}`, `${unique[1]}`]);
+  }
+}
+
+/** The fraction of predictions that were correct */
+export function accuracyScore(cMatrix: ConfusionMatrix): number {
+  return cMatrix.true / cMatrix.size;
+}
+/** The fraction of "positive" predictions that were actually positive */
+export function precisionScore(cMatrix: ConfusionMatrix): number {
+  return cMatrix.truePositive / (cMatrix.truePositive + cMatrix.falsePositive);
+}
+/** The fraction of positives that were predicted correctly */
+export function sensitivityScore(cMatrix: ConfusionMatrix): number {
+  return cMatrix.truePositive / (cMatrix.truePositive + cMatrix.falseNegative);
+}
+/** The fraction of positives that were predicted correctly */
+export const recallScore = sensitivityScore;
+/** The fraction of negatives that were predicted correctly */
+export function specificityScore(cMatrix: ConfusionMatrix): number {
+  return cMatrix.trueNegative / (cMatrix.trueNegative + cMatrix.falsePositive);
+}
+/** Compute F1 Score */
+export function f1Score(cMatrix: ConfusionMatrix): number {
+  return (
+    (2 * cMatrix.truePositive) /
+    (2 * cMatrix.truePositive + cMatrix.falsePositive + cMatrix.falseNegative)
+  );
+}
+
+/** Compute Cohen's Kappa to find Agreement */
+export function cohensKappa(cMatrix: ConfusionMatrix): number {
+  const actualAgreement = accuracyScore(cMatrix);
+  const expectedAgreement =
+    ((cMatrix.truePositive + cMatrix.falsePositive) / cMatrix.size) *
+      ((cMatrix.truePositive + cMatrix.falseNegative) / cMatrix.size) +
+    ((cMatrix.falsePositive + cMatrix.trueNegative) / cMatrix.size) *
+      ((cMatrix.falseNegative + cMatrix.trueNegative) / cMatrix.size);
+  return (actualAgreement - expectedAgreement) / (1 - expectedAgreement);
+}
diff --git a/packages/utilities/src/metrics/mod.ts b/packages/utilities/src/metrics/mod.ts
new file mode 100644
index 0000000..15b7232
--- /dev/null
+++ b/packages/utilities/src/metrics/mod.ts
@@ -0,0 +1,7 @@
+/**
+ * Metrics for Machine Learning outcomes.
+ * @module
+ */
+
+export * from "./classification.ts";
+export * from "./regression.ts";
diff --git a/packages/utilities/src/metrics/regression.ts b/packages/utilities/src/metrics/regression.ts
new file mode 100644
index 0000000..a2cce5a
--- /dev/null
+++ b/packages/utilities/src/metrics/regression.ts
@@ -0,0 +1,37 @@
+/** Mean Absolute Error */
+export function mae(y: ArrayLike<number>, y1: ArrayLike<number>): number {
+  let err = 0;
+  for (let i = 0; i < y.length; i += 1) {
+    err += (y[i] - y1[i]) ** 2;
+  }
+  return err / y.length;
+}
+
+/** Mean Square Error */
+export function mse(y: ArrayLike<number>, y1: ArrayLike<number>): number {
+  let err = 0;
+  for (let i = 0; i < y.length; i += 1) {
+    err += (y[i] - y1[i]) ** 2;
+  }
+  return err / y.length;
+}
+
+/** Root Mean Square Error */
+export function rmse(y: ArrayLike<number>, y1: ArrayLike<number>): number {
+  return Math.sqrt(mse(y, y1));
+}
+
+/** R2 Score for regression */
+export function r2(y: ArrayLike<number>, y1: ArrayLike<number>): number {
+  let mean = 0;
+  for (let i = 0; i < y.length; i += 1) {
+    mean += y[i];
+  }
+  mean /= y.length;
+  let ssr = 0, sst = 0;
+  for (let i = 0; i < y.length; i += 1) {
+    ssr += Math.pow(y1[i] - mean, 2);
+    sst += Math.pow(y[i] - mean, 2);
+  }
+  return ssr / sst;
+}
diff --git a/packages/utilities/src/mod.ts b/packages/utilities/src/mod.ts
new file mode 100644
index 0000000..8a2ce59
--- /dev/null
+++ b/packages/utilities/src/mod.ts
@@ -0,0 +1,5 @@
+export * from "./image/mod.ts";
+export * from "./utils/mod.ts";
+export * from "./metrics/mod.ts";
+export * from "./encoding/mod.ts";
+export * from "./text/mod.ts";
diff --git a/packages/utilities/src/text/mod.ts b/packages/utilities/src/text/mod.ts
new file mode 100644
index 0000000..84e8bda
--- /dev/null
+++ b/packages/utilities/src/text/mod.ts
@@ -0,0 +1,7 @@
+/**
+ * Text-related utilities for Machine Learning
+ * and Data Science.
+ * @module
+ */
+
+export * from "./preprocess/mod.ts";
diff --git a/packages/utilities/src/text/preprocess/cleaner.ts b/packages/utilities/src/text/preprocess/cleaner.ts
new file mode 100644
index 0000000..575bd28
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/cleaner.ts
@@ -0,0 +1,53 @@
+import type { StandardizeConfig } from "../../utils/common_types.ts";
+
+/** Simple text cleaner */
+export class TextCleaner implements StandardizeConfig {
+  stripHtml: boolean;
+  lowercase: boolean;
+  normalizeWhiteSpaces: boolean;
+  stripNewlines: boolean;
+  constructor({
+    stripHtml = false,
+    lowercase = false,
+    normalizeWhiteSpaces = true,
+    stripNewlines = true,
+  }: StandardizeConfig = {}) {
+    this.stripHtml = stripHtml;
+    this.lowercase = lowercase;
+    this.normalizeWhiteSpaces = normalizeWhiteSpaces;
+    this.stripNewlines = stripNewlines;
+  }
+  clean(text: string): string;
+  clean(text: string[]): string[];
+  clean(text: string | string[]) {
+    if (Array.isArray(text)) {
+      return text.map((line) => preprocess(line, this));
+    }
+    return preprocess(text, this);
+  }
+}
+
+/** Function for quick cleaning of text */
+export function preprocess(
+  text: string,
+  {
+    stripHtml = false,
+    lowercase = false,
+    normalizeWhiteSpaces = true,
+    stripNewlines = true,
+  }: StandardizeConfig = {},
+): string {
+  if (lowercase) {
+    text = text.toLowerCase();
+  }
+  if (stripHtml) {
+    text = text.replace(/<([^>]+)>/g, " ");
+  }
+  if (stripNewlines) {
+    text = text.replace(/\n/g, " ");
+  }
+  if (normalizeWhiteSpaces) {
+    text = text.replace(/\s\s+/g, " ");
+  }
+  return text;
+}
diff --git a/packages/utilities/src/text/preprocess/mod.ts b/packages/utilities/src/text/preprocess/mod.ts
new file mode 100644
index 0000000..246a7c3
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/mod.ts
@@ -0,0 +1,29 @@
+import type {
+  Cleaner,
+  Tokenizer,
+  Transformer,
+  Vectorizer,
+} from "../../utils/common_types.ts";
+
+// import { TextCleaner } from "./cleaner.ts";
+// import { SplitTokenizer } from "./tokenize/mod.ts";
+// import { CountVectorizer } from "./vectorize/mod.ts";
+// import { TfIdfTransformer } from "./transformer/mod.ts";
+
+/** TODO */
+interface PreprocessorConfig {
+  vectorizer: Vectorizer;
+  tokenizer: Tokenizer;
+  cleaner: Cleaner;
+  transformer: Transformer;
+};
+
+/** TODO */
+export class TextPreprocessor implements Partial<PreprocessorConfig> {
+  // todo
+}
+
+export * from "./cleaner.ts";
+export * from "./tokenize/mod.ts";
+export * from "./transformer/mod.ts";
+export * from "./vectorize/mod.ts";
diff --git a/packages/utilities/src/text/preprocess/tokenize/mod.ts b/packages/utilities/src/text/preprocess/tokenize/mod.ts
new file mode 100644
index 0000000..80a9941
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/tokenize/mod.ts
@@ -0,0 +1 @@
+export { SplitTokenizer } from "./split.ts";
diff --git a/packages/utilities/src/text/preprocess/tokenize/split.ts b/packages/utilities/src/text/preprocess/tokenize/split.ts
new file mode 100644
index 0000000..046d370
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/tokenize/split.ts
@@ -0,0 +1,112 @@
+import { DefaultIgnoreList } from "../../../constants/stop_words.ts";
+import type { BaseTokenizerOptions } from "../../../utils/common_types.ts";
+
+/** Tokenize text based on separator (whitespace) */
+export class SplitTokenizer {
+  /** Words to ignore from vocabulary */
+  skipWords: "english" | false | string[];
+  /** Configuration / Function for preprocessing */
+  vocabulary: Map<string, number>;
+  /** An internal counter for remembering the last index in vocabulary. */
+  #lastToken: Uint32Array;
+  constructor(
+    options: Partial<BaseTokenizerOptions & { indices: boolean }> = {},
+  ) {
+    this.skipWords = options.skipWords ?? false;
+    this.vocabulary = options.vocabulary ?? new Map();
+    this.#lastToken = new Uint32Array(1);
+    if (options.indices && !this.vocabulary.size) {
+      this.#lastToken[0] = 2;
+      this.vocabulary.set("__pad__", 0);
+      this.vocabulary.set("__unk__", 1);
+    }
+    if (this.vocabulary.size) {
+      this.#lastToken[0] = this.vocabulary.size;
+    }
+  }
+  get lastToken(): number {
+    return Atomics.load(this.#lastToken, 0);
+  }
+  /** Construct a vocabulary from a given set of text. */
+  fit(text: string | string[]): this {
+    if (Array.isArray(text)) {
+      let i = 0;
+      while (i < text.length) {
+        this.fit(text[i]);
+        i += 1;
+      }
+    } else {
+      const words = this.split(text);
+      let i = 0;
+      while (i < words.length) {
+        if (!this.vocabulary.has(words[i])) {
+          if (this.skipWords === "english") {
+            if (DefaultIgnoreList.includes(words[i])) {
+              i += 1;
+              continue;
+            }
+          } else if (Array.isArray(this.skipWords)) {
+            if (this.skipWords.includes(words[i])) {
+              i += 1;
+              continue;
+            }
+          }
+          const token = this.#incrementToken();
+          this.vocabulary.set(words[i], token);
+        }
+        i += 1;
+      }
+    }
+    return this;
+  }
+  #incrementToken(): number {
+    return Atomics.add(this.#lastToken, 0, 1);
+  }
+  /**
+   * Convert a document (string | array of strings) into vectors.
+   */
+  transform(text: string | string[]): number[][] {
+    if (!this.vocabulary.size) {
+      throw new Error(
+        "Tokenizer vocabulary not initialized yet. Call `Tokenizer()` with a custom vocabulary or use `.fit()` on text.",
+      );
+    }
+    if (Array.isArray(text)) {
+      const size = Math.max(...text.map((x) => this.split(x).length));
+      const res = Array(text.length);
+      let i = 0;
+      while (i < text.length) {
+        res[i] = this.#transform(text[i], size);
+        i += 1;
+      }
+      return res;
+    } else {
+      return [this.#transform(text, 0)];
+    }
+  }
+  #transform(text: string, size: number): number[] {
+    const words = this.split(text);
+    if (!size) size = words.length;
+    const res = new Array(size);
+    res.fill(this.vocabulary.get("__pad__") || 0);
+    let i = 0;
+    while (i < words.length && i < size) {
+      if (this.vocabulary.has(words[i])) {
+        const index = this.vocabulary.get(words[i]);
+        if (typeof index === "number") {
+          res[i] = index;
+        } else {
+          res[i] = this.vocabulary.get("__unk__") || 0;
+        }
+      } else {
+        res[i] = this.vocabulary.get("__unk__") || 0;
+      }
+      i += 1;
+    }
+    return res;
+  }
+  // TODO: Support custom split modes
+  split(text: string): string[] {
+    return text.split(" ");
+  }
+}
diff --git a/packages/utilities/src/text/preprocess/transformer/mod.ts b/packages/utilities/src/text/preprocess/transformer/mod.ts
new file mode 100644
index 0000000..c10dbfe
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/transformer/mod.ts
@@ -0,0 +1 @@
+export { TfIdfTransformer } from "./tfidf.ts";
diff --git a/packages/utilities/src/text/preprocess/transformer/tfidf.ts b/packages/utilities/src/text/preprocess/transformer/tfidf.ts
new file mode 100644
index 0000000..cef8ea4
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/transformer/tfidf.ts
@@ -0,0 +1,42 @@
+import type { DataType } from "../../../utils/common_types.ts";
+import type { Matrix, MatrixLike } from "../../../mod.ts";
+import { multiplyDiags } from "../../../utils/math.ts";
+
+/** Convert tf features (CountVectorizer) into tf-idf features. */
+export class TfIdfTransformer {
+  idf: null | Float64Array;
+  constructor({ idf }: { idf?: Float64Array } = {}) {
+    this.idf = idf ?? null;
+  }
+  /**
+   * Get idf matrix from tf features.
+   * @param data tf features from CountVectorizer
+   * @returns Tf-Idf transformer
+   */
+  fit<T extends DataType>(data: Matrix<T>): TfIdfTransformer {
+    const shape = {
+      features: data.nCols,
+      samples: data.nRows,
+    };
+    const freq = data.rowSum();
+
+    const idf = new Float64Array(freq.length);
+
+    let i = 0;
+    while (i < idf.length) {
+      idf[i] = Math.log(shape.samples / Number(freq[i])) + 1;
+      i += 1;
+    }
+    this.idf = idf;
+    return this;
+  }
+  /**
+   * Transform an tf features into tf-idf features.
+   * @param data tf features from CountVectorizer
+   * @returns Sparse matrix of Tf-Idf features
+   */
+  transform<T extends DataType>(data: MatrixLike<T>): Matrix<T> {
+    if (this.idf === null) throw new Error("IDF not initialized yet.");
+    return multiplyDiags(data, this.idf);
+  }
+}
diff --git a/packages/utilities/src/text/preprocess/vectorize/count_vectorizer.ts b/packages/utilities/src/text/preprocess/vectorize/count_vectorizer.ts
new file mode 100644
index 0000000..189fe0f
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/vectorize/count_vectorizer.ts
@@ -0,0 +1,40 @@
+import type { DataType, DType } from "../../../utils/common_types.ts";
+import { getConstructor } from "../../../utils/mod.ts";
+import { Matrix } from "../../../mod.ts";
+
+/**
+ * Convert tokens into vectors based on term frequency
+ */
+export class CountVectorizer {
+  #vocabSize: number;
+  constructor(vocabSize: number) {
+    this.#vocabSize = vocabSize;
+  }
+  /**
+   * Convert a document (string | array of strings) into vectors.
+   */
+  transform<T extends DataType>(tokens: number[][], dType: T): Matrix<T> {
+    if (!this.#vocabSize) {
+      throw new Error("Vocab not initialized.");
+    }
+    const res = new Matrix(dType, [tokens.length, this.#vocabSize]);
+    let i = 0;
+    while (i < tokens.length) {
+      res.setRow(i, this.#transform<T>(tokens[i], dType));
+      i += 1;
+    }
+    return res as Matrix<T>;
+  }
+  #transform<T extends DataType>(tokens: number[], dType: T): DType<T> {
+    const res = new (getConstructor<T>(dType))(this.#vocabSize);
+    let i = 0;
+    while (i < tokens.length) {
+      if (tokens[i] < this.#vocabSize) {
+        // @ts-ignore No error here
+        res[tokens[i]] += typeof res[tokens[i]] === "bigint" ? 1n : 1;
+      }
+      i += 1;
+    }
+    return res as DType<T>;
+  }
+}
diff --git a/packages/utilities/src/text/preprocess/vectorize/mod.ts b/packages/utilities/src/text/preprocess/vectorize/mod.ts
new file mode 100644
index 0000000..2f9fa65
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/vectorize/mod.ts
@@ -0,0 +1,2 @@
+export { CountVectorizer } from "./count_vectorizer.ts";
+export { MultiHotVectorizer } from "./multi_hot.ts";
diff --git a/packages/utilities/src/text/preprocess/vectorize/multi_hot.ts b/packages/utilities/src/text/preprocess/vectorize/multi_hot.ts
new file mode 100644
index 0000000..ba7800a
--- /dev/null
+++ b/packages/utilities/src/text/preprocess/vectorize/multi_hot.ts
@@ -0,0 +1,39 @@
+import type { DataType, DType } from "../../../utils/common_types.ts";
+import { getConstructor } from "../../../utils/mod.ts";
+import { Matrix } from "../../../mod.ts";
+
+/**
+ * Convert tokens into vectors based on term frequency
+ */
+export class MultiHotVectorizer {
+  #vocabSize: number;
+  constructor(vocabSize: number) {
+    this.#vocabSize = vocabSize;
+  }
+  /**
+   * Convert a document (string | array of strings) into vectors.
+   */
+  transform<T extends DataType>(tokens: number[][], dType: T): Matrix<T> {
+    if (!this.#vocabSize) {
+      throw new Error("Vocab not initialized.");
+    }
+    const res = new Matrix(dType, [tokens.length, this.#vocabSize]);
+    let i = 0;
+    while (i < tokens.length) {
+      res.setRow(i, this.#transform<T>(tokens[i], dType));
+      i += 1;
+    }
+    return res as Matrix<T>;
+  }
+  #transform<T extends DataType>(tokens: number[], dType: T): DType<T> {
+    const res = new (getConstructor<T>(dType))(this.#vocabSize);
+    let i = 0;
+    while (i < tokens.length) {
+      if (tokens[i] < this.#vocabSize) {
+        res[tokens[i]] = typeof res[tokens[i]] === "bigint" ? 1n : 1;
+      }
+      i += 1;
+    }
+    return res as DType<T>;
+  }
+}
diff --git a/packages/utilities/src/utils/array/mod.ts b/packages/utilities/src/utils/array/mod.ts
new file mode 100644
index 0000000..38ed82f
--- /dev/null
+++ b/packages/utilities/src/utils/array/mod.ts
@@ -0,0 +1,7 @@
+/**
+ * Utilities for working with arrays in appraisal.
+ * @module
+ */
+export { useRange, useSeries } from "./range.ts";
+export { useUnique } from "./unique.ts";
+export { useSplit } from "./split.ts";
diff --git a/packages/utilities/src/utils/array/range.ts b/packages/utilities/src/utils/array/range.ts
new file mode 100644
index 0000000..f92243c
--- /dev/null
+++ b/packages/utilities/src/utils/array/range.ts
@@ -0,0 +1,38 @@
+/**
+ * Get n evenly distributed numbers in a range.
+ * @param n Number of numbers to generate.
+ * @param min Lower limit of range (inclusive).
+ * @param max Upper limit of range (exclusive).
+ * @returns Array of n evenly distributed numbers.
+ */
+export function useRange(n: number, min = 0, max = 1): number[] {
+  const res = new Array(n);
+  let i = 0;
+  while (i < n) {
+    res[i] = min + ((i * (max - min)) / n);
+    i += 1;
+  }
+  return res;
+}
+
+/**
+ * Get an array of numbers between a given range,
+ * incremented by a step.
+ * @param min Lower limit of range (inclusive).
+ * @param max Upper limit of range (exclusive).
+ * @param step step to increment by
+ * @returns Array of numbers
+ */
+export function useSeries(max: number): number[];
+export function useSeries(min: number, max: number, step?: number): number[];
+export function useSeries(min: number, max?: number, step = 1): number[] {
+  if (typeof max === "undefined") [min, max] = [0, min];
+  const res = new Array(~~((max - min) / step));
+  res[0] = min;
+  let i = 1;
+  while (i < res.length) {
+    res[i] = res[i - 1] + step;
+    i += 1;
+  }
+  return res;
+}
diff --git a/packages/utilities/src/utils/array/split.ts b/packages/utilities/src/utils/array/split.ts
new file mode 100644
index 0000000..790d0ca
--- /dev/null
+++ b/packages/utilities/src/utils/array/split.ts
@@ -0,0 +1,49 @@
+import type { Sliceable } from "../common_types.ts";
+import { useShuffle } from "../random/shuffle.ts";
+
+interface SplitOptions {
+  ratio: [number, number];
+  shuffle?: boolean;
+}
+
+/** Split arrays by their first axis */
+export function useSplit<T extends Sliceable[]>(
+  options: SplitOptions = { ratio: [7, 3], shuffle: false },
+  ...arr: T
+): [typeof arr, typeof arr] {
+  if (!arr.every((x) => x.length === arr[0].length)) {
+    throw new Error("All arrays must have equal length!");
+  }
+  const { ratio, shuffle } = options;
+  const idx = Math.floor(arr[0].length * (ratio[0] / (ratio[0] + ratio[1])));
+  if (!shuffle) {
+    return [arr.map((x) => x.slice(0, idx)), arr.map((x) => x.slice(idx))] as [
+      T,
+      T,
+    ];
+  } else {
+    const shuffled = useShuffle(0, arr[0].length);
+    const x1 = shuffled.slice(0, idx);
+    const x2 = shuffled.slice(idx);
+    return [
+      arr.map((x) =>
+        x.filter(
+          ((_, i, __) => x1.includes(i)) as (
+            value: unknown,
+            index: number,
+            array: unknown[],
+          ) => value is typeof x,
+        )
+      ) as typeof arr,
+      arr.map((x) =>
+        x.filter(
+          ((_, i, __) => x2.includes(i)) as (
+            value: unknown,
+            index: number,
+            array: unknown[],
+          ) => value is typeof x,
+        )
+      ) as typeof arr,
+    ];
+  }
+}
diff --git a/packages/utilities/src/utils/array/unique.ts b/packages/utilities/src/utils/array/unique.ts
new file mode 100644
index 0000000..6a4dcea
--- /dev/null
+++ b/packages/utilities/src/utils/array/unique.ts
@@ -0,0 +1,10 @@
+/**
+ * Remove duplicate values in an array.
+ * Uses a strict = for identifying duplicates.
+ * @param {T[]} arr Array to remove duplicates from.
+ * @returns {T[]} Array with only unique elements.
+ */
+export function useUnique<T>(arr: ArrayLike<T>): T[] {
+  const array = Array.from(arr);
+  return array.filter((x, i) => array.indexOf(x) === i);
+}
diff --git a/packages/utilities/src/utils/common_types.ts b/packages/utilities/src/utils/common_types.ts
new file mode 100644
index 0000000..d373484
--- /dev/null
+++ b/packages/utilities/src/utils/common_types.ts
@@ -0,0 +1,231 @@
+import type { Matrix } from "./mod.ts";
+
+export type DataType =
+  | "u8"
+  | "u16"
+  | "u32"
+  | "u64"
+  | "i8"
+  | "i16"
+  | "i32"
+  | "i64"
+  | "f32"
+  | "f64";
+
+export interface TypedArrayMapping {
+  u8: Uint8Array;
+  u16: Uint16Array;
+  u32: Uint32Array;
+  u64: BigUint64Array;
+  i8: Int8Array;
+  i16: Int16Array;
+  i32: Int32Array;
+  i64: BigInt64Array;
+  f32: Float32Array;
+  f64: Float64Array;
+}
+
+export interface TypedArrayConstructorMapping {
+  u8: Uint8ArrayConstructor;
+  u16: Uint16ArrayConstructor;
+  u32: Uint32ArrayConstructor;
+  u64: BigUint64ArrayConstructor;
+  i8: Int8ArrayConstructor;
+  i16: Int16ArrayConstructor;
+  i32: Int32ArrayConstructor;
+  i64: BigInt64ArrayConstructor;
+  f32: Float32ArrayConstructor;
+  f64: Float64ArrayConstructor;
+}
+
+interface TypedArrayValueMapping {
+  u8: number;
+  u16: number;
+  u32: number;
+  u64: bigint;
+  i8: number;
+  i16: number;
+  i32: number;
+  i64: bigint;
+  f32: number;
+  f64: number;
+}
+
+export type DTypeValue<T extends keyof TypedArrayValueMapping> = T extends
+  keyof TypedArrayValueMapping ? TypedArrayValueMapping[T] : never;
+
+type AddableTypes = number | bigint;
+
+export type AddDTypeValues<
+  T1 extends AddableTypes,
+  T2 extends AddableTypes,
+> = T1 extends number ? T2 extends number ? number
+  : T2 extends bigint ? bigint
+  : never
+  : T1 extends bigint ? T2 extends number ? bigint
+    : T2 extends bigint ? bigint
+    : never
+  : never;
+
+export type DType<T extends keyof TypedArrayMapping> = T extends
+  keyof TypedArrayMapping ? TypedArrayMapping[T] : never;
+
+export type DTypeConstructor<T extends keyof TypedArrayConstructorMapping> =
+  T extends keyof TypedArrayConstructorMapping ? TypedArrayConstructorMapping[T]
+    : never;
+
+export type TypedArray =
+  | Uint8Array
+  | Uint16Array
+  | Uint32Array
+  | BigUint64Array
+  | Int8Array
+  | Int16Array
+  | Int32Array
+  | BigInt64Array
+  | Float32Array
+  | Float64Array;
+
+export type Constructor<T> = new (length: number) => T;
+
+export interface Sliceable {
+  filter(
+    predicate: (
+      value: unknown,
+      index: number,
+      array: unknown[],
+    ) => value is unknown,
+  ): Sliceable;
+  slice(start?: number, end?: number): Sliceable;
+  length: number;
+}
+
+export function getDataType<DT extends DataType>(data: DType<DT>): DT {
+  return (
+    data instanceof Uint8Array
+      ? "u8"
+      : data instanceof Uint16Array
+      ? "u16"
+      : data instanceof Uint32Array
+      ? "u32"
+      : data instanceof Int8Array
+      ? "i8"
+      : data instanceof Int16Array
+      ? "i16"
+      : data instanceof Int32Array
+      ? "i32"
+      : data instanceof Float32Array
+      ? "f32"
+      : data instanceof Float64Array
+      ? "f64"
+      : "u8"
+  ) as DT; // shouldn't reach "u8"
+}
+
+export interface Image2d {
+  /** Width of the image */
+  width: number;
+  /** Height of the image */
+  height: number;
+  /**
+   * Number of channels in the image
+   * For a regular RGBA image, the value
+   * will be 4.
+   */
+  channels: number;
+  /** Array of length width * height * channels */
+  data: Uint8ClampedArray;
+}
+
+export interface Patch2d {
+  /** Width of the patch */
+  width: number;
+  /** Height of the patch */
+  height: number;
+}
+export interface PatchCollection extends Patch2d {
+  /**
+   * Number of channels in the image
+   * For a regular RGBA image, the value
+   * will be 4.
+   */
+  channels: number;
+  /** Number of patches in the collection */
+  size: number;
+  data: Uint8ClampedArray;
+}
+
+export type Pixel = [number, number, number, number?];
+
+export interface StandardizeConfig {
+  /** Whether to convert everything to lowercase before fitting / transforming */
+  lowercase?: boolean;
+  /** Whether to strip HTML tags */
+  stripHtml?: boolean;
+  /** Whether to replace multiple whitespaces. */
+  normalizeWhiteSpaces?: boolean;
+  /** Strip Newlines */
+  stripNewlines?: boolean;
+}
+
+export type VectorizerMode = "count" | "indices" | "multihot" | "tfidf";
+
+export type VectorizerModeConfig =
+  | {
+    mode: "count";
+    config?: Partial<BaseVectorizerOptions>;
+  }
+  | {
+    mode: "indices";
+    config?: Partial<BaseVectorizerOptions & { size: number }>;
+  }
+  | {
+    mode: "multihot";
+    config?: Partial<BaseVectorizerOptions>;
+  }
+  | {
+    mode: "tfidf";
+    config?: Partial<BaseVectorizerOptions & { idf: Float64Array }>;
+  };
+
+export interface TokenizerModeConfig {
+  mode: "whitespace";
+  config?: Partial<BaseVectorizerOptions>;
+}
+
+export interface BaseVectorizerOptions {
+  /** Map words to indices */
+  vocabulary: Map<string, number>;
+  /** Options for standardizing text */
+  standardize: StandardizeConfig | ((s: string) => string);
+  /** Words to ignore from vocabulary */
+  skipWords: "english" | false | string[];
+}
+
+export interface BaseTokenizerOptions {
+  /** Map words to indices */
+  vocabulary: Map<string, number>;
+  /** Options for standardizing text */
+  standardize: StandardizeConfig | ((s: string) => string);
+  /** Words to ignore from vocabulary */
+  skipWords: "english" | false | string[];
+}
+
+export interface Tokenizer {
+  fit(text: string | string[]): unknown;
+  transform(text: string | string[]): number[];
+}
+
+export interface Cleaner {
+  clean(text: string): string;
+  clean(text: string[]): string[];
+}
+
+export interface Vectorizer {
+  transform<T extends DataType>(tokens: number[][], dType: T): Matrix<T>;
+}
+
+export interface Transformer {
+  fit<T extends DataType>(data: Matrix<T>): Transformer;
+  transform<T extends DataType>(data: Matrix<T>): Matrix<T>;
+}
diff --git a/packages/utilities/src/utils/math.ts b/packages/utilities/src/utils/math.ts
new file mode 100644
index 0000000..cf52d1b
--- /dev/null
+++ b/packages/utilities/src/utils/math.ts
@@ -0,0 +1,31 @@
+import type { DataType } from "./common_types.ts";
+import { Matrix, type MatrixLike } from "./misc/matrix.ts";
+
+/** A very basic, low-effort multiplication. */
+export function multiplyDiags<T extends DataType>(
+  x: MatrixLike<T>,
+  y: ArrayLike<number>,
+): Matrix<T> {
+  const res = new Matrix(x);
+  if (y.length !== res.nCols) {
+    throw new Error(
+      `Expected diagonal vector of shape (${res.nCols}, 1). Found (${y.length}, 1).`,
+    );
+  }
+  let i = 0;
+  while (i < res.nRows) {
+    const offset = i * res.nCols;
+    let j = 0;
+    while (j < y.length) {
+      res.setCell(
+        i,
+        j,
+        // @ts-ignore types will always match
+        x.data[offset + j] * (typeof res[0] === "bigint" ? BigInt(y[j]) : y[j]),
+      );
+      j += 1;
+    }
+    i += 1;
+  }
+  return res as Matrix<T>;
+}
diff --git a/packages/utilities/src/utils/misc/get_constructor.ts b/packages/utilities/src/utils/misc/get_constructor.ts
new file mode 100644
index 0000000..42b30c9
--- /dev/null
+++ b/packages/utilities/src/utils/misc/get_constructor.ts
@@ -0,0 +1,30 @@
+import type { DataType, DTypeConstructor } from "../common_types.ts";
+
+export function getConstructor<DT extends DataType>(
+  dType: DT,
+): DTypeConstructor<DT> {
+  switch (dType) {
+    case "u8":
+      return Uint8Array as DTypeConstructor<DT>;
+    case "u16":
+      return Uint16Array as DTypeConstructor<DT>;
+    case "u32":
+      return Uint32Array as DTypeConstructor<DT>;
+    case "u64":
+      return BigUint64Array as DTypeConstructor<DT>;
+    case "i8":
+      return Int8Array as DTypeConstructor<DT>;
+    case "i16":
+      return Int16Array as DTypeConstructor<DT>;
+    case "i32":
+      return Int32Array as DTypeConstructor<DT>;
+    case "i64":
+      return BigInt64Array as DTypeConstructor<DT>;
+    case "f32":
+      return Float32Array as DTypeConstructor<DT>;
+    case "f64":
+      return Float64Array as DTypeConstructor<DT>;
+    default:
+      throw new Error(`Unknown data type ${dType}.`);
+  }
+}
diff --git a/packages/utilities/src/utils/misc/image.ts b/packages/utilities/src/utils/misc/image.ts
new file mode 100644
index 0000000..7e6d2c8
--- /dev/null
+++ b/packages/utilities/src/utils/misc/image.ts
@@ -0,0 +1,101 @@
+import type { Pixel } from "../common_types.ts";
+
+export type ImageData = {
+  data: Uint8ClampedArray;
+  width: number;
+  height: number;
+  channels: number; // always 4, for compat with retraigo/vectorizer
+  colorSpace: "srgb" | "display-p3";
+};
+
+type ImageOptions = {
+  data: Uint8ClampedArray;
+  width: number;
+  height?: number;
+  channels?: number;
+};
+
+export class Image implements ImageData {
+  data: Uint8ClampedArray;
+  width: number;
+  height: number;
+  channels: number; // always 4, for compat with retraigo/vectorizer
+  colorSpace: "srgb" | "display-p3";
+  constructor(data: ImageOptions) {
+    this.data = Uint8ClampedArray.from(data.data);
+    // N-channels is always 4
+    this.channels = 4;
+    this.width = data.width;
+    this.height = data.height ??
+      this.data.length / (this.width * this.channels);
+    // If height is not an integer or width is incorrect
+    if (this.height !== ~~this.height) {
+      throw new TypeError(
+        `Height must be an integer. Received ${this.height}.`,
+      );
+    }
+    // Only srgb is supported
+    this.colorSpace = "srgb";
+  }
+  get pixels(): number {
+    return this.width * this.height;
+  }
+  getNthPixel(n: number): [number, number, number, number] {
+    const offset = n << 2;
+    return [
+      this.data[offset],
+      this.data[offset + 1],
+      this.data[offset + 2],
+      this.data[offset + 3],
+    ];
+  }
+  getPixel(row: number, col: number): Pixel {
+    if (row >= this.height) {
+      throw new RangeError(
+        `Requested row ${row} is outside of bounds 0..${this.height}.`,
+      );
+    }
+    if (col >= this.width) {
+      throw new RangeError(
+        `Requested column ${col} is outside of bounds 0..${this.width}.`,
+      );
+    }
+    const offset = row * this.width + col;
+    const [r, g, b, a] = this.data.slice(offset, offset + 4);
+    return [r, g, b, a];
+  }
+  setPixel(row: number, col: number, [r, g, b, a]: Pixel) {
+    if (row >= this.height) {
+      throw new RangeError(
+        `Requested row ${row} is outside of bounds 0..${this.height}.`,
+      );
+    }
+    if (col >= this.width) {
+      throw new RangeError(
+        `Requested column ${col} is outside of bounds 0..${this.width}.`,
+      );
+    }
+    const offset = row * this.width + col;
+    this.data.set(typeof a !== "undefined" ? [r, g, b, a] : [r, g, b], offset);
+  }
+  updatePixel(
+    row: number,
+    col: number,
+    color: Pixel,
+  ) {
+    if (row >= this.height) {
+      throw new RangeError(
+        `Requested row ${row} is outside of bounds 0..${this.height}.`,
+      );
+    }
+    if (col >= this.width) {
+      throw new RangeError(
+        `Requested column ${col} is outside of bounds 0..${this.width}.`,
+      );
+    }
+    const offset = row * this.width + col;
+    for (let i = 0; i < color.length; i += 1) {
+      this.data[offset + i] += color[i] ?? 0;
+    }
+  }
+}
diff --git a/packages/utilities/src/utils/misc/matrix.ts b/packages/utilities/src/utils/misc/matrix.ts
new file mode 100644
index 0000000..677353a
--- /dev/null
+++ b/packages/utilities/src/utils/misc/matrix.ts
@@ -0,0 +1,304 @@
+/**
+ * A 2D Tensor with more methods.
+ * @module
+ */
+
+import type {
+  AddDTypeValues,
+  DataType,
+  DType,
+  DTypeConstructor,
+  DTypeValue,
+  Sliceable,
+} from "../common_types.ts";
+import { type NDArray, type Shape, Tensor, type TensorLike } from "./tensor.ts";
+
+/** The base type implemented by Matrix */
+export type MatrixLike<DT extends DataType> = {
+  /** Raw 1D TypedArray supplied */
+  data: DType<DT>;
+  /** Number of rows, columns */
+  shape: Shape<2>;
+};
+
+/**
+ * Class for 2D Arrays.
+ * This is not akin to a mathematical Matrix (a collection of column vectors).
+ * This is a collection of row vectors.
+ * A special case of Tensor for 2D data.
+ */
+export class Matrix<DT extends DataType> extends Tensor<DT, 2>
+  implements Sliceable, MatrixLike<DT> {
+  /**
+   * Create a matrix from a typed array
+   * @param data Data to move into the matrix.
+   * @param shape [rows, columns] of the matrix.
+   */
+  constructor(matrix: TensorLike<DT, 2>);
+  constructor(array: NDArray<DT>[2], dType: DT);
+  constructor(data: DType<DT>, shape: Shape<2>);
+  constructor(dType: DT, shape: Shape<2>);
+  constructor(
+    data: NDArray<DT>[2] | DType<DT> | DT | TensorLike<DT, 2>,
+    shape?: Shape<2> | DT,
+  ) {
+    // @ts-ignore This call will work
+    super(data, shape);
+  }
+  /** Convert the Matrix into a HTML table */
+  get html(): string {
+    let res = "<table>\n";
+    res += "<thead><tr><DTh>idx</th>";
+    for (let i = 0; i < this.nCols; i += 1) {
+      res += `<DTh>${i}</th>`;
+    }
+    res += "</tr></thead>";
+    let j = 0;
+    for (const row of this.rows()) {
+      res += `<tr><td><strong>${j}</strong></td>`;
+      j += 1;
+      for (const x of row) {
+        res += `<td>${x}</td>`;
+      }
+      res += "</tr>";
+    }
+    res += "</table>";
+    return res;
+  }
+  get length(): number {
+    return this.nRows;
+  }
+  /** Returns number of cols */
+  get nCols(): number {
+    return this.shape[1];
+  }
+  /** Returns number of rows */
+  get nRows(): number {
+    return this.shape[0];
+  }
+  /** Get the transpose of the matrix. This method clones the matrix. */
+  get T(): Matrix<DT> {
+    const resArr = new (this.data.constructor as DTypeConstructor<DT>)(
+      this.nRows * this.nCols,
+    ) as DType<DT>;
+    let i = 0;
+    for (const col of this.cols()) {
+      // @ts-ignore This line will work
+      resArr.set(col, i * this.nRows);
+      i += 1;
+    }
+    return new Matrix(resArr, this.shape);
+  }
+  /** Get a pretty version for printing. DO NOT USE FOR MATRICES WITH MANY COLUMNS. */
+  get pretty(): string {
+    let res = "";
+    for (const row of this.rows()) {
+      res += row.join("\t");
+      res += "\n";
+    }
+    return res;
+  }
+  /** Alias for row */
+  at(pos: number): DType<DT> {
+    return this.row(pos);
+  }
+  /** Get the nth column in the matrix */
+  col(n: number): DType<DT> {
+    let i = 0;
+    const col = new (this.data.constructor as DTypeConstructor<DT>)(
+      this.nRows,
+    ) as DType<DT>;
+    let offset = 0;
+    while (i < this.nRows) {
+      col[i] = this.data[offset + n];
+      i += 1;
+      offset += this.nCols;
+    }
+    return col;
+  }
+  colMean(): DType<DT> {
+    const sum = this.colSum();
+    let i = 0;
+    const divisor = (
+      typeof this.data[0] === "bigint" ? BigInt(this.nCols) : this.nCols
+    ) as DTypeValue<DT>;
+    while (i < sum.length) {
+      sum[i] = (sum[i] as DTypeValue<DT>) / divisor;
+      i += 1;
+    }
+    return sum;
+  }
+  /** Get a column array of all column sums in the matrix */
+  colSum(): DType<DT> {
+    const sum = new (this.data.constructor as DTypeConstructor<DT>)(
+      this.nRows,
+    ) as DType<DT>;
+    let i = 0;
+    while (i < this.nCols) {
+      let j = 0;
+      while (j < this.nRows) {
+        // @ts-ignore I'll fix this later
+        sum[j] = (sum[j] + this.item(j, i)) as AddDTypeValues<
+          DTypeValue<DT>,
+          DTypeValue<DT>
+        >;
+        j += 1;
+      }
+      i += 1;
+    }
+    return sum;
+  }
+  /** Get the dot product of two matrices */
+  dot(rhs: Matrix<DT>): number | bigint {
+    if (rhs.nRows !== this.nRows) {
+      throw new Error("Matrices must have equal rows.");
+    }
+    if (rhs.nCols !== this.nCols) {
+      throw new Error("Matrices must have equal cols.");
+    }
+    let res = (typeof this.data[0] === "bigint" ? 0n : 0) as DTypeValue<DT>;
+    let j = 0;
+    while (j < this.nCols) {
+      let i = 0;
+      while (i < this.nRows) {
+        const adder = (this.item(i, j) as DTypeValue<DT>) *
+          (rhs.item(i, j) as DTypeValue<DT>);
+        // @ts-ignore I'll fix this later
+        res += adder as DTypeValue<DT>;
+        i += 1;
+      }
+      j += 1;
+    }
+    return res;
+  }
+  /** Filter the matrix by rows */
+  filter(
+    fn: (value: DType<DT>, row: number, _: DType<DT>[]) => boolean,
+  ): Matrix<DT> {
+    const satisfying: number[] = [];
+    let i = 0;
+    while (i < this.nRows) {
+      if (fn(this.row(i), i, [])) {
+        satisfying.push(i);
+      }
+      i += 1;
+    }
+    const matrix = new Matrix(this.dType, [satisfying.length, this.nCols]);
+    i = 0;
+    while (i < satisfying.length) {
+      // @ts-ignore This line will work
+      matrix.setRow(i, this.row(satisfying[i]));
+      i += 1;
+    }
+    return matrix;
+  }
+  /** Get an item using a row and column index */
+  item(row: number, col: number): DTypeValue<DT> {
+    return this.data[row * this.nCols + col] as DTypeValue<DT>;
+  }
+  /** Get the nth row in the matrix */
+  row(n: number): DType<DT> {
+    return this.data.slice(n * this.nCols, (n + 1) * this.nCols) as DType<DT>;
+  }
+  rowMean(): DType<DT> {
+    const sum = this.rowSum();
+    let i = 0;
+    const divisor = (
+      typeof this.data[0] === "bigint" ? BigInt(this.nRows) : this.nRows
+    ) as DTypeValue<DT>;
+    while (i < sum.length) {
+      sum[i] = (sum[i] as DTypeValue<DT>) / divisor;
+      i += 1;
+    }
+    return sum;
+  }
+  /** Compute the sum of all rows */
+  rowSum(): DType<DT> {
+    const sum = new (this.data.constructor as DTypeConstructor<DT>)(
+      this.nCols,
+    ) as DType<DT>;
+    let i = 0;
+    let offset = 0;
+    while (i < this.nRows) {
+      let j = 0;
+      while (j < this.nCols) {
+        // @ts-ignore This line will work
+        sum[j] += this.data[offset + j];
+        j += 1;
+      }
+      i += 1;
+      offset += this.nCols;
+    }
+    return sum;
+  }
+  /**
+   * Add a value to an existing element
+   * Will throw an error if the types mismatch
+   */
+  setAdd(row: number, col: number, val: number | bigint) {
+    // @ts-expect-error Must provide appropriate number/bigint argument
+    this.data[row * this.nCols + col] += val;
+  }
+  /** Replace a column */
+  setCol(col: number, val: ArrayLike<number>): number {
+    let i = 0;
+    while (i < this.nRows) {
+      this.data[i * this.nCols + col] = val[i];
+      i += 1;
+    }
+    return col;
+  }
+  /** Set a value in the matrix */
+  setCell(row: number, col: number, val: number) {
+    this.data[row * this.nCols + col] = val;
+  }
+  /** Replace a row */
+  setRow(row: number, val: ArrayLike<number> | ArrayLike<bigint>) {
+    // @ts-expect-error Must provide appropriate number/bigint argument
+    this.data.set(val, row * this.nCols);
+  }
+  /** Slice matrix by rows */
+  slice(start = 0, end?: number): Matrix<DT> {
+    return new Matrix<DT>(
+      this.data.slice(
+        start ? start * this.nCols : 0,
+        end ? end * this.nCols : undefined,
+      ) as DType<DT>,
+      [end ? end - start : this.nRows - start, this.nCols],
+    );
+  }
+  /** Iterate through rows */
+  *rows(): Generator<DType<DT>> {
+    let i = 0;
+    while (i < this.nRows) {
+      yield this.data.slice(i * this.nCols, (i + 1) * this.nCols) as DType<DT>;
+      i += 1;
+    }
+  }
+  /** Iterate through columns */
+  *cols(): Generator<DType<DT>> {
+    let i = 0;
+    while (i < this.nCols) {
+      let j = 0;
+      const col = new (this.data.constructor as DTypeConstructor<DT>)(
+        this.nRows,
+      ) as DType<DT>;
+      while (j < this.nRows) {
+        col[j] = this.data[j * this.nCols + i];
+        j += 1;
+      }
+      yield col;
+      i += 1;
+    }
+  }
+
+  [Symbol.for("Jupyter.display")](): Record<string, string> {
+    return {
+      // Plain text content
+      "text/plain": this.pretty,
+
+      // HTML output
+      "text/html": this.html,
+    };
+  }
+}
diff --git a/packages/utilities/src/utils/misc/mod.ts b/packages/utilities/src/utils/misc/mod.ts
new file mode 100644
index 0000000..4e1e713
--- /dev/null
+++ b/packages/utilities/src/utils/misc/mod.ts
@@ -0,0 +1,4 @@
+export * from "./get_constructor.ts";
+export * from "./image.ts";
+export * from "./matrix.ts";
+export * from "./tensor.ts";
diff --git a/packages/utilities/src/utils/misc/tensor.ts b/packages/utilities/src/utils/misc/tensor.ts
new file mode 100644
index 0000000..98b738f
--- /dev/null
+++ b/packages/utilities/src/utils/misc/tensor.ts
@@ -0,0 +1,201 @@
+/**
+ * Multi-dimensional representation of data.
+ * @module
+ */
+
+import {
+  type DataType,
+  type DType,
+  type DTypeValue,
+  getDataType,
+  type Sliceable,
+} from "../common_types.ts";
+import { getConstructor } from "./get_constructor.ts";
+
+/** Order of the tensor */
+export type Order = 1 | 2 | 3 | 4 | 5 | 6;
+
+/** The base type implemented by Tensor */
+export interface TensorLike<DT extends DataType, O extends Order> {
+  data: DType<DT>;
+  shape: Shape<O>;
+};
+
+/** An array with n items */
+export type Shape<N extends number> = N extends 0 ? []
+  : [number, ...number[]] & { length: N };
+
+/** nDArray type */
+export interface NDArray<DT extends DataType> {
+  1: DTypeValue<DT>[];
+  2: DTypeValue<DT>[][];
+  3: DTypeValue<DT>[][][];
+  4: DTypeValue<DT>[][][][];
+  5: DTypeValue<DT>[][][][][];
+  6: DTypeValue<DT>[][][][][][];
+};
+
+function getShape<O extends Order, DT extends DataType>(
+  arr: NDArray<DT>[O],
+): Shape<O> {
+  const shape: number[] = [];
+  let curr: NDArray<DT>[O] | DTypeValue<DT> = arr;
+  while (Array.isArray(curr)) {
+    shape.push(curr.length);
+    curr = curr[0] as NDArray<DT>[O] | DTypeValue<DT>;
+  }
+  return shape as Shape<O>;
+}
+
+/**
+ * A Tensor of order O.
+ */
+export class Tensor<DT extends DataType, O extends Order>
+  implements Sliceable, TensorLike<DT, O> {
+  order: O;
+  shape: Shape<O>;
+  data: DType<DT>;
+  strides: Shape<O>;
+  dType: DT;
+  constructor(tensor: TensorLike<DT, O>);
+  constructor(array: NDArray<DT>[O], dType: DT);
+  constructor(data: DType<DT>, shape: Shape<O>);
+  constructor(dType: DT, shape: Shape<O>);
+  constructor(
+    data: NDArray<DT>[O] | DType<DT> | DT | TensorLike<DT, O>,
+    shape?: Shape<O> | DType<DT>,
+  ) {
+    if (typeof data === "string") {
+      if (!shape || !Array.isArray(shape)) {
+        throw new Error(
+          `Expected shape to be defined as an Array. Got ${shape}.`,
+        );
+      } else {
+        this.data = new (getConstructor(data))(
+          shape.reduce((acc, val) => acc * val, 1),
+        ) as DType<DT>;
+        this.shape = shape;
+        this.order = shape.length as O;
+        this.dType = data;
+        this.strides = Tensor.getStrides(this.shape);
+      }
+    } else if (Array.isArray(data)) {
+      if (!shape || typeof shape !== "string") {
+        throw new Error(
+          `Expected dType to be defined when using a normal array. Got ${shape}.`,
+        );
+      } else {
+        this.shape = getShape(data);
+        this.order = this.shape.length as O;
+        // @ts-ignore They're mapped correctly
+        this.data = getConstructor(shape).from(
+          data.flat(this.shape.length) as DTypeValue<DT>[],
+        ) as DType<DT>;
+        this.dType = shape;
+        this.strides = Tensor.getStrides(this.shape);
+      }
+    } else if (ArrayBuffer.isView(data)) {
+      if (!shape || !Array.isArray(shape)) {
+        throw new Error(
+          `Shape must be defined when Tensor is constructed from a TypedArray.`,
+        );
+      }
+      this.shape = shape;
+      this.order = this.shape.length as O;
+      this.data = data;
+      this.dType = getDataType(data);
+      this.strides = Tensor.getStrides(this.shape);
+    } else if (data.shape) {
+      this.data = data.data;
+      this.shape = data.shape;
+      this.dType = getDataType(data.data);
+      this.order = this.shape.length as O;
+      this.strides = Tensor.getStrides(this.shape);
+    } else {
+      throw new Error("Tensor initialization does not follow any overload.");
+    }
+  }
+  /** For compat with useSplit() */
+  get length(): number {
+    return this.shape[0];
+  }
+  /** Filter the tensor by 0th axis */
+  filter(
+    fn: (value: DType<DT>, row: number, _: DType<DT>[]) => boolean,
+  ): Tensor<DT, O> {
+    const satisfying: number[] = [];
+    let i = 0;
+    const stride = this.strides[0];
+    while (i < this.shape[0]) {
+      if (
+        fn(this.data.slice(stride * i, stride * (i + 1)) as DType<DT>, i, [])
+      ) {
+        satisfying.push(i);
+      }
+      i += 1;
+    }
+    const res = new Tensor<DT, O>(this.dType, [
+      satisfying.length,
+      ...this.shape.slice(1),
+    ] as Shape<O>);
+    i = 0;
+    while (i < satisfying.length) {
+      res.data.set(
+        // @ts-ignore This line will work
+        this.data.slice(stride * satisfying[i], stride * (satisfying[i] + 1)),
+        i,
+      );
+      i += 1;
+    }
+    return res;
+  }
+  /** Get an item using indices */
+  item(...indices: number[]): DTypeValue<DT> {
+    return this.data[
+      indices.reduce((acc, val, i) => acc + val * this.strides[i])
+    ] as DTypeValue<DT>;
+  }
+  /** Slice matrix by axis */
+  slice(start = 0, end?: number, axis = 0): Tensor<DT, O> {
+    if (axis > this.strides.length - 1) {
+      throw new Error(
+        `Axis given is ${axis} while highest axis is ${
+          this.strides.length - 1
+        }.`,
+      );
+    }
+    if (!end) end = this.shape[axis] - start;
+    const stride = this.strides[axis - 1] || this.length;
+    const newStride = (stride / this.shape[axis]) * (end - start);
+    const res = new Tensor<DT, O>(this.dType, [
+      ...this.shape.slice(0, axis),
+      end - start,
+      ...this.shape.slice(axis + 1),
+    ] as Shape<O>);
+    for (let i = 0; i < this.length / stride; i += 1) {
+      // @ts-ignore The values will always match
+      res.data.set(
+        // @ts-ignore The values will always match
+        this.data
+          .slice(stride * i, stride * (i + 1))
+          .slice(start * this.strides[axis], end * this.strides[axis]),
+        newStride * i,
+      );
+    }
+    return res;
+  }
+  toJSON(): { data: DTypeValue<DT>[]; shape: Shape<O> } {
+    return {
+      // @ts-ignore I have no idea why TS is doing this
+      data: Array.from(this.data) as DTypeValue<DT>[],
+      shape: this.shape,
+    };
+  }
+  static getStrides<O extends Order>(shape: Shape<O>): Shape<O> {
+    const strides = new Array(shape.length).fill(1);
+    for (let i = 0; i < shape.length; i += 1) {
+      strides[i] = shape.slice(i + 1).reduce((acc, val) => acc * val, 1);
+    }
+    return strides as Shape<O>;
+  }
+}
diff --git a/packages/utilities/src/utils/mod.ts b/packages/utilities/src/utils/mod.ts
new file mode 100644
index 0000000..606c346
--- /dev/null
+++ b/packages/utilities/src/utils/mod.ts
@@ -0,0 +1,3 @@
+export * from "./random/mod.ts";
+export * from "./array/mod.ts";
+export * from "./misc/mod.ts";
diff --git a/packages/utilities/src/utils/random/mod.ts b/packages/utilities/src/utils/random/mod.ts
new file mode 100644
index 0000000..31d700f
--- /dev/null
+++ b/packages/utilities/src/utils/random/mod.ts
@@ -0,0 +1,5 @@
+export { useNormal, useNormalArray } from "./normal.ts";
+export { useRearrange } from "./rearrange.ts";
+export { useRNG } from "./rng.ts";
+export { useShuffle } from "./shuffle.ts";
+export { useWeighted } from "./weighted.ts";
diff --git a/packages/utilities/src/utils/random/normal.ts b/packages/utilities/src/utils/random/normal.ts
new file mode 100644
index 0000000..4729cea
--- /dev/null
+++ b/packages/utilities/src/utils/random/normal.ts
@@ -0,0 +1,53 @@
+const TWO_PI = Math.PI * 2;
+
+/**
+ * A **Normal** or **Gaussian** distribution is a type of
+ * continuous probability distribution dependent on two
+ * parameters:
+ *
+ * **μ** - The **mean**
+ * **σ** - The **standard deviation**
+ *
+ * This implementation makes use of the popular Box-Muller transform.
+ */
+
+/**
+ * Generate a normal random variate.
+ * @param mean Mean of the distribution μ.
+ * @param stddev Standard Deviation of the distribution σ.
+ * @returns A normal random variate.
+ */
+export function useNormal(
+  mean: number,
+  stddev: number,
+): [number, number] {
+  const u = [Math.random(), Math.random()];
+
+  const m = Math.sqrt(-2.0 * Math.log(u[0]));
+  return [
+    (stddev * m * Math.cos(TWO_PI * u[1])) + mean,
+    (stddev * m * Math.sin(TWO_PI * u[1])) + mean,
+  ];
+}
+
+/**
+ * Generate a normally distributed array.
+ * @param mean Mean of the distribution μ.
+ * @param variance Variance of the distribution σ^2.
+ * @returns A normally distributed array.
+ */
+
+export function useNormalArray(
+  num: number,
+  mean: number,
+  variance: number,
+): Float32Array {
+  const result = new Float32Array(num);
+  let i = 0;
+  const stddev = Math.sqrt(variance);
+  while (i < num) {
+    result[i] = useNormal(mean, stddev)[0];
+    ++i;
+  }
+  return result;
+}
diff --git a/packages/utilities/src/utils/random/rearrange.ts b/packages/utilities/src/utils/random/rearrange.ts
new file mode 100644
index 0000000..7e980fc
--- /dev/null
+++ b/packages/utilities/src/utils/random/rearrange.ts
@@ -0,0 +1,13 @@
+import { useShuffle } from "./shuffle.ts";
+
+/**
+ * Rearrange characters in a string randomly.
+ * @param {number|string} n Number / String to rearrange.
+ * @returns {number|string} Number / String rearranged randomly.
+ */
+export function useRearrange(n: number | string): number | string {
+  const res = (typeof n === "number" ? n.toString() : n).split("");
+  const shuffled = useShuffle(res).join("");
+  return typeof n === "number" ? Number(shuffled) : shuffled;
+}
+export default useRearrange;
diff --git a/packages/utilities/src/utils/random/rng.ts b/packages/utilities/src/utils/random/rng.ts
new file mode 100644
index 0000000..2ac97a3
--- /dev/null
+++ b/packages/utilities/src/utils/random/rng.ts
@@ -0,0 +1,15 @@
+/**
+ * Get random number from range
+ * @param min Min value of range.
+ * @param max Max value of range.
+ * @param allowDecimals Whether to allow decimal point in result.
+ * @returns Random number
+ */
+export function useRNG(
+  min: number,
+  max: number,
+  allowDecimals = false,
+): number {
+  const rng = min + (Math.random() * (max - min));
+  return allowDecimals ? rng : Math.round(rng);
+}
diff --git a/packages/utilities/src/utils/random/shuffle.ts b/packages/utilities/src/utils/random/shuffle.ts
new file mode 100644
index 0000000..0c3daec
--- /dev/null
+++ b/packages/utilities/src/utils/random/shuffle.ts
@@ -0,0 +1,39 @@
+import { useSeries } from "../array/range.ts";
+
+/**
+ * Shuffle a given array in-place.
+ * @param arr Array to shuffle
+ */
+export function useShuffle<T>(arr: T[]): T[];
+/** Get a shuffled array of numbers from 0 to the given number */
+export function useShuffle(max: number): number[];
+/** Get a shuffled array of numbers between the given range */
+export function useShuffle(min: number, max: number): number[];
+export function useShuffle<T>(
+  maybeArr: number | T[],
+  max?: number,
+): T[] | number[] {
+  if (Array.isArray(maybeArr)) {
+    const idx = useShuffle(0, maybeArr.length);
+    const res = new Array(maybeArr.length);
+    let i = 0;
+    while (i < maybeArr.length) {
+      res[i] = maybeArr[idx[i]];
+      i += 1;
+    }
+    return res;
+  } else {
+    const min = typeof max !== "undefined" ? maybeArr : 0;
+    const max1 = typeof max !== "undefined" ? max : maybeArr;
+
+    const arr = useSeries(min, max1);
+
+    let i = arr.length - 1;
+    while (i >= 1) {
+      const j = Math.floor(Math.random() * i);
+      [arr[i], arr[j]] = [arr[j], arr[i]];
+      i -= 1;
+    }
+    return arr;
+  }
+}
diff --git a/packages/utilities/src/utils/random/weighted.ts b/packages/utilities/src/utils/random/weighted.ts
new file mode 100644
index 0000000..ff70989
--- /dev/null
+++ b/packages/utilities/src/utils/random/weighted.ts
@@ -0,0 +1,29 @@
+// Check out https://github.com/retraigo/fortuna
+
+export interface WeightedChoice<ItemType> {
+  result: ItemType;
+  chance: number;
+}
+
+/**
+ * Roll one from an array of weighted choices.
+ * @param {WeightedChoice[]} choices - Choices to roll from.
+ * @param {number} totalChance - Sum of all chance properties.
+ * @returns {WeightedChoice} Item rolled.
+ */
+
+export function useWeighted<ItemType>(
+  choices: WeightedChoice<ItemType>[],
+): WeightedChoice<ItemType> {
+  const total = choices.reduce(
+    (acc: number, val: WeightedChoice<ItemType>) => acc + val.chance,
+    0,
+  );
+  const result = Math.random() * total;
+  let going = 0.0;
+  for (let i = 0; i < choices.length; ++i) {
+    going += choices[i].chance;
+    if (result < going) return choices[i];
+  }
+  return choices[Math.floor(Math.random() * choices.length)];
+}
diff --git a/src/backends/wasm/lib/netsaur_bg.wasm b/src/backends/wasm/lib/netsaur_bg.wasm
deleted file mode 100644
index 99ea6d9..0000000
Binary files a/src/backends/wasm/lib/netsaur_bg.wasm and /dev/null differ
diff --git a/tokenizers/lib/netsaur_tokenizers_bg.wasm b/tokenizers/lib/netsaur_tokenizers_bg.wasm
deleted file mode 100644
index 2782196..0000000
Binary files a/tokenizers/lib/netsaur_tokenizers_bg.wasm and /dev/null differ
diff --git a/web.ts b/web.ts
index d588f70..8760c53 100644
--- a/web.ts
+++ b/web.ts
@@ -1,10 +1,10 @@
-export { setupBackend } from "./src/core/engine.ts";
-export * from "./src/core/mod.ts";
-export * from "./src/core/types.ts";
-export * from "./src/core/tensor/tensor.ts";
-export * from "./src/core/api/layers.ts";
-export * from "./src/core/api/shape.ts";
-export * from "./src/core/api/network.ts";
-export * from "./src/core/api/optimizer.ts";
+export { setupBackend } from "./packages/core/core/engine.ts";
+export * from "./packages/core/core/mod.ts";
+export * from "./packages/core/core/types.ts";
+export * from "./packages/core/core/tensor/tensor.ts";
+export * from "./packages/core/core/api/layers.ts";
+export * from "./packages/core/core/api/shape.ts";
+export * from "./packages/core/core/api/network.ts";
+export * from "./packages/core/core/api/optimizer.ts";
 
-export { WASM } from "./src/backends/wasm/mod.ts";
+export { WASM } from "./packages/core/backends/wasm/mod.ts";