This repository provides Jupyter notebooks for the book "Deep Learning with PyTorch Step-by-Step," targeting beginners in deep learning. It offers a practical, code-driven approach to learning PyTorch, enabling users to reproduce book examples and build foundational knowledge.

How It Works

The project consists of individual Jupyter notebooks, each corresponding to a chapter in the book. These notebooks contain executable code that demonstrates core deep learning concepts and PyTorch implementations, allowing users to follow along and replicate the book's results directly.

Quick Start & Requirements

• Installation: Clone the repository (git clone https://github.com/dvgodoy/PyTorchStepByStep.git) and run Jupyter notebooks (jupyter notebook).
• Prerequisites: Anaconda (Python 3.x), PyTorch (CPU or GPU version), TensorBoard, Git. Optional: GraphViz and torchviz for model visualization.
• Setup: Detailed setup instructions are provided, including creating a Conda environment (conda create -n pytorchbook anaconda, conda activate pytorchbook).
• Resources: Google Colab and Binder are offered as cloud-based alternatives for running notebooks without local setup.
• Links: Book Website, Volume I Changes, Volume II Changes, Volume III Changes

Highlighted Details

• Comprehensive coverage from fundamentals to NLP and computer vision.
• Revised for PyTorch 2.x, addressing library updates.
• Cloud execution options via Google Colab and Binder for ease of access.
• Detailed local installation guide using Anaconda for flexibility.

Maintenance & Community

The repository is the official companion to a published book, indicating a stable and curated content base. Further community interaction details are not explicitly provided in the README.

Licensing & Compatibility

The repository code is likely subject to the book's licensing terms, which are not specified in the README. Compatibility for commercial use or closed-source linking would depend on these terms.

Limitations & Caveats

The optional GraphViz installation for model visualization can be challenging, particularly on Windows, and may require manual PATH configuration. Some cells might not function if GraphViz is not successfully installed.