diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 12b7f9d..a25511e 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -29,11 +29,11 @@ In this example, the file :samp:`main.py` contains a function :samp:`my_task` th
    def my_task(ctx: mlxp.Context)->None:
 
      # Displaying user-defined options from './configs/config.yaml
-     print("ctx.config")
+     print(ctx.config)
 
      # Logging information in log directory created by MLXP: (here "./logs/1" )
-     for i in range(ctx.config.num_epoch)
-        ctx.logger.log_metrics({"epoch":i})
+     for i in range(ctx.config.num_epoch):
+        ctx.logger.log_metrics({"epoch":i}, log_name="Quickstart")
 
 
 
diff --git a/docs/tutorial_introduction.rst b/docs/tutorial_introduction.rst
index 4de8199..79b2086 100644
--- a/docs/tutorial_introduction.rst
+++ b/docs/tutorial_introduction.rst
@@ -19,22 +19,22 @@ The first step is to create a directory 'tutorial' containing the code needed fo
    tutorial/
    ├── configs/
    │   └── config.yaml
-   ├── core.py
+   ├── core_app.py
    ├── main.py
    └── read.py
 
-The directory contains three files: :samp:`core.py`, :samp:`main.py` :samp:`results.py`. It also contains a directory :samp:`configs` that will be used later by MLXP. For now, we will only have a look at the :samp:`core.py` and :samp:`main.py` files.
+The directory contains three files: :samp:`core_app.py`, :samp:`main.py` :samp:`read.py`. It also contains a directory :samp:`configs` that will be used later by MLXP. For now, we will only have a look at the :samp:`core_app.py` and :samp:`main.py` files.
 
 
-The :samp:`core.py` file
+The :samp:`core_app.py` file
 """"""""""""""""""""""""
 
-The file :samp:`core.py` contains a PyTorch implementation of a one hidden layer network :samp:`OneHiddenLayer` as well as a simple data loader :samp:`DataLoader` that we will use during training. 
-In the rest of the tutorial, we will not need to worry about the content of :samp:`core.py`, but let's just have a quick look at this file:
+The file :samp:`core_app.py` contains a PyTorch implementation of a one hidden layer network :samp:`OneHiddenLayer` as well as a simple data loader :samp:`DataLoader` that we will use during training. 
+In the rest of the tutorial, we will not need to worry about the content of :samp:`core_app.py`, but let's just have a quick look at this file:
 
 
 .. code-block:: python
-    :caption: main.py
+    :caption: core_app.py
 
     import torch
     import torch.nn as nn
diff --git a/tutorial/README.rst b/tutorial/README.rst
index 253be81..449de66 100644
--- a/tutorial/README.rst
+++ b/tutorial/README.rst
@@ -4,7 +4,7 @@ MLXP Tutorial
 This page presents the main functionalities of MLXP through a tutorial.
 See the following page for more detailed information:
 
--  `Tutorial <https://inria-thoth.github.io/mlxp/tutorial.html>`__
+-  `Tutorial <https://inria-thoth.github.io/mlxp/pages/master/tutorial.html>`__
 
 Requirements
 ------------
diff --git a/tutorial/core_app.py b/tutorial/core_app.py
index a162477..f687bcb 100644
--- a/tutorial/core_app.py
+++ b/tutorial/core_app.py
@@ -27,8 +27,8 @@ def __init__(self, d_int, device, normalize=False):
 		self.X = torch.normal(mean= torch.zeros(N_samples,d_int,dtype=dtype,device=device),std=1.)
 
 		if normalize:			
-			inv_norm = 1./tr.norm(self.X,dim=1)
-			self.X = tr.einsum('nd,n->nd',self.X,inv_norm)
+			inv_norm = 1./torch.norm(self.X,dim=1)
+			self.X = torch.einsum('nd,n->nd',self.X,inv_norm)
 
 		self.total_size = N_samples