Deep Learning from Scratch 🧠

Welcome to Deep Learning from Scratch, a repository where I implement fundamental deep learning architectures from scratch using Python, NumPy, PyTorch, and TensorFlow. This project aims to provide a deeper understanding of how neural networks function internally, without relying on high-level libraries.

Contents

1. Foundational Deep Neural Networks

Papers

• DNN (1987): Learning Internal Representations by Error Propagation pdf
• CNN (1989): Backpropagation Applied to Handwritten Zip Code Recognition pdf
• LeNet (1998): Gradient-Based Learning Applied to Document Recognition pdf
• AlexNet (2012): ImageNet Classification with Deep Convolutional Networks pdf
• U-Net (2015): Convolutional Networks for Biomedical Image Segmentation pdf

2. Optimization and Regularization Techniques

Papers

• Weight Decay (1991): A Simple Weight Decay Can Improve Generalization pdf
• ReLU (2011): Deep Sparse Rectified Neural Networks pdf
• Residuals (2015): Deep Residual Learning for Image Recognition pdf
• Dropout (2014): Preventing Neural Networks from Overfitting pdf
• BatchNorm (2015): Accelerating Deep Network Training pdf
• LayerNorm (2016): Layer Normalization pdf
• GELU (2016): Gaussian Error Linear Units pdf
• Adam (2014): Stochastic Optimization Method pdf

3. Sequence Modeling

Papers

• RNN (1989): Continually Running Fully Recurrent Neural Networks pdf
• LSTM (1997): Long-Short Term Memory pdf
• Learning to Forget (2000): Continual Prediction with LSTM pdf
• Word2Vec (2013): Word Representations in Vector Space pdf
• Phrase2Vec (2013): Distributed Representations of Words and Phrases pdf
• Encoder-Decoder (2014): RNN Encoder-Decoder for Machine Translation pdf
• Seq2Seq (2014): Sequence to Sequence Learning pdf
• Attention (2014): Neural Machine Translation with Alignment pdf
• Mixture of Experts (2017): Sparsely-Gated Neural Networks pdf

4. Language Modeling

Papers

• Transformer (2017): Attention Is All You Need pdf
• BERT (2018): Bidirectional Transformers for Language Understanding pdf
• RoBERTa (2019): Robustly Optimized BERT Pretraining pdf
• T5 (2019): Unified Text-to-Text Transformer pdf
• GPT Series:
  • GPT (2018): Generative Pre-Training pdf
  • GPT-2 (2018): Unsupervised Multitask Learning pdf
  • GPT-3 (2020): Few-Shot Learning pdf
  • GPT-4 (2023): Advanced Language Model pdf
• LoRA (2021): Low-Rank Adaptation of Large Language Models pdf
• RLHF (2019): Fine-Tuning from Human Preferences pdf
• InstructGPT (2022): Following Instructions with Human Feedback pdf
• Vision Transformer (2020): Image Recognition with Transformers pdf
• ELECTRA (2020): Discriminative Pre-training pdf

5. Image Generative Modeling

Papers

• GAN (2014): Generative Adversarial Networks pdf
• VAE (2013): Auto-Encoding Variational Bayes pdf
• VQ VAE (2017): Neural Discrete Representation Learning pdf
• Diffusion Models:
  • Initial Diffusion (2015): Nonequilibrium Thermodynamics pdf
  • Denoising Diffusion (2020): Probabilistic Models pdf
  • Improved Denoising Diffusion (2021) pdf
• CLIP (2021): Visual Models from Natural Language Supervision pdf
• DALL-E (2021-2022): Text-to-Image Generation pdf
• SimCLR (2020): Contrastive Learning of Visual Representations pdf

6. Deep Reinforcement Learning

Papers

• Deep Reinforcement Learning (2017): Mastering Chess and Shogi pdf
• Deep Q-Learning (2013): Playing Atari Games pdf
• AlphaGo (2016): Mastering the Game of Go pdf
• AlphaFold (2021): Protein Structure Prediction pdf

7. Additional Influential Papers

• Deep Learning Survey (2015): By LeCun, Bengio, and Hinton pdf
• BigGAN (2018): Large Scale GAN Training pdf
• WaveNet (2016): Generative Model for Raw Audio pdf
• BERTology (2020): Survey of BERT Use Cases pdf

Scaling and Model Optimization

• Scaling Laws for Neural Language Models (2020): Predicting Model Performance pdf
• Chinchilla (2022): Training Compute-Optimal Large Language Models pdf
• Gopher (2022): Scaling Language Models with Massive Compute pdf

Fine-tuning and Adaptation

• P-Tuning (2021): Prompt Tuning with Soft Prompts pdf
• Prefix-Tuning (2021): Optimizing Continuous Prompts pdf
• AdaLoRA (2023): Adaptive Low-Rank Adaptation pdf
• QLoRA (2023): Efficient Fine-Tuning of Quantized Models pdf

Inference and Optimization Techniques

• FlashAttention (2022): Fast and Memory-Efficient Attention pdf
• FlashAttention-2 (2023): Faster Attention Mechanism pdf
• Direct Preference Optimization (DPO) (2023): Aligning Language Models with Human Preferences pdf
• LoRA (2021): Low-Rank Adaptation of Large Language Models pdf

Pre-training and Model Architecture

• Mixture of Experts (MoE) (2022): Scaling Language Models with Sparse Experts pdf
• GLaM (2021): Efficient Scaling with Mixture of Experts pdf
• Switch Transformers (2022): Scaling to Trillion Parameter Models pdf

Reasoning and Capabilities

• Chain of Thought Prompting (2022): Reasoning with Language Models pdf
• Self-Consistency (2022): Improving Language Model Reasoning pdf
• Tree of Thoughts (2023): Deliberate Problem Solving pdf

Efficiency and Compression

• DistilBERT (2019): Distilled Version of BERT pdf
• Knowledge Distillation (2022): Comprehensive Survey pdf
• Pruning and Quantization Techniques (2022): Model Compression Survey pdf

🛠️ How to Use

1. Clone the repository:

   git clone https://github.com/Ishaan-Ansari/Deep-Learning-from-scratch.git

2. Navigate to a specific model:

   cd Deep-Learning-from-scratch/[Folder_Name]

3. Run Jupyter notebooks:

   jupyter notebook

4. Follow the instructions within each notebook.

📌 Contributions & Feedback

This project is a work in progress! If you have suggestions, feel free to fork the repo, submit issues, or create pull requests.

⭐ If you find this helpful, star this repository and stay tuned for more updates!

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This keeps it clean, structured, and informative. Let me know if you need modifications! 🚀