Dive into Deep Learning – Exercises Solutions

This repository contains my solutions and implementations of the exercises from the book Dive into Deep Learning (D2L).
The aim is to build a strong foundation in deep learning by coding every exercise from scratch.

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📚 About the Book

Dive into Deep Learning (D2L) is an open-source, interactive book that teaches deep learning through both theory and practical coding.
It covers:

• Mathematics (linear algebra, probability, optimization)
• Core machine learning concepts
• Deep neural networks (MLPs, CNNs, RNNs, Attention, Transformers)
• Real-world applications (NLP, Computer Vision, RL)
• Performance and deployment

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🗂 Repository Structure

This repo is organized chapter by chapter:

├── Chapter01_Introduction/
├── Chapter02_Preliminaries/
├── Chapter03_Linear_Neural_Networks/
├── Chapter04_Multilayer_Perceptrons/
├── Chapter05_Deep_Learning_Computation/
├── Chapter06_CNN/
├── Chapter07_Modern_CNN/
├── Chapter08_Recurrent_Neural_Networks/
├── Chapter09_Attention/
├── Chapter10_Optimization/
├── Chapter11_Computational_Performance/
├── Chapter12_Computer_Vision/
├── Chapter13_NLP/
└── Chapter14_Reinforcement_Learning/

Each folder contains:

• notebooks/ → Jupyter notebooks with solutions
• code/ → Python scripts for reusable implementations
• README.md → Quick notes for that chapter

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⚙️ Setup Instructions

1. Clone the repo:

   git clone https://github.com/your-username/d2l-exercises.git
   cd d2l-exercises

2. (Optional) Create a virtual environment:

   python -m venv d2l-env
   source d2l-env/bin/activate   # Linux/Mac
   d2l-env\Scripts\activate      # Windows

3. Install dependencies:

   pip install -r requirements.txt

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🛠 Requirements

• Python 3.8+
• Jupyter Notebook / JupyterLab
• PyTorch (or MXNet/TensorFlow depending on your chosen backend)
• Matplotlib, NumPy, pandas
• d2l package (pip install d2l)