pytorch-book - Thorough Introduction to PyTorch Offering Practical Examples and Advanced Techniques

Introduction to the PyTorch-Book Project

Overview

The PyTorch-Book project serves as a comprehensive code supplement for the book "Deep Learning Framework PyTorch: Getting Started and Practice (2nd Edition)". Not only is it a part of this book, but it also stands alone as an independent introduction and tutorial for PyTorch. This project is ideal for those seeking to learn PyTorch from the ground up, offering guidance through basic usage, advanced extensions, and real-world applications.

New Edition Enhancements

This second edition has undergone a significant upgrade from the first edition, with its entire code base now written for PyTorch version 1.8. Based on feedback from numerous readers, the content has been extensively updated to cover three major modules: basic usage, advanced extensions, and practical applications.

Content Breakdown

Basic Usage (Chapters 2–5)

These chapters are dedicated to explaining the core aspects of PyTorch, focusing on its primary modules and the commonly used tools in deep learning. By utilizing Jupyter Notebook as a teaching aid, readers are encouraged to modify, run, and experiment with the code for a hands-on learning experience.

Chapter 2: Discusses the installation of PyTorch and the setup of the learning environment. It provides an overview of the major components of PyTorch to help readers get acquainted with its functionality.
Chapter 3: Covers the use of Tensors and the autograd system in PyTorch, demonstrating how to implement linear regression and contrasting their differences. This chapter delves into the structure of Tensors and the principles of autograd, offering a comprehensive understanding of PyTorch’s foundational modules.
Chapter 4: Introduces the basics of PyTorch's neural network module nn, including layers, activation functions, loss functions, and optimizers. It concludes with a practical exercise to implement the classic ResNet architecture in under 50 lines of code.
Chapter 5: Explores PyTorch's data loading, pretrained models, visualization tools, and GPU acceleration features, which can significantly enhance programming efficiency.

Advanced Extensions (Chapters 6–8)

These chapters focus on advanced aspects of PyTorch designed to help users write more efficient programs.

Chapter 6: Introduces vectorization concepts, covering broadcasting rules and indexing techniques, and guides readers through implementing convolution operations, intersection over union, RoI Align, and backward unique operations using vectorization.
Chapter 7: Discusses distributed computations and parallel processing, explaining their principles and guiding the use of torch.distributed and Horovod for distributed PyTorch training.
Chapter 8: Explores CUDA extensions by showing how to implement a Sigmoid function with CUDA, summarizing the relationship between CUDA, NVIDIA drivers, cuDNN, and Python.

Practical Applications (Chapters 9–13)

In these chapters, the book delves into various intriguing applications using PyTorch. The repository provides complete implementation codes and pretrained models for readers to test.

Chapter 9: Serves as a transition chapter aimed at consolidating knowledge from earlier chapters through a classic Kaggle image classification competition, focusing on organizing and debugging code.
Chapter 10: Covers the fundamentals of Generative Adversarial Networks (GANs) and leads readers in building an anime face generator.
Chapter 11: Introduces basic natural language processing concepts and the principles of CharRNN and Transformers, guiding readers through building a program that can automatically generate poetry or acrostic poems.
Chapter 12: Explains the principles of style transfer, directing readers in implementing a neural network capable of performing arbitrary style transfers on images.
Chapter 13: Details the principles of object detection and introduces the CenterNet algorithm, a single-stage approach devoid of anchor boxes and non-maximum suppression. The concepts can be extended to 3D object detection, human pose estimation, and trajectory tracking.

Purchasing the Book

While the book is not necessary to benefit from this tutorial, as over 60% of the book’s text and 90% of the code are available in the repository, owning a beautifully printed, full-color edition may enhance the reading experience.

Technical Details

The code has been primarily tested with Python 3 and PyTorch versions 1.6 to 1.8. Testing has not been conducted for Python 2 or other recent PyTorch versions.
Contributions, bug reports, or improvements through issues and pull requests are welcomed.

Getting Started

Install PyTorch by choosing the appropriate version from the official website.

Clone the repository:

git clone https://github.com/chenyuntc/PyTorch-book.git

For any bugs, unclear explanations, or confusion, users are encouraged to open issues or submit pull requests. The project welcomes collaborative efforts and contributions from the community. Happy coding!