Stage 4: Deep Learning and Neural Networks

PyTorch Handbook Project Detailed Introduction

Project Overview

PyTorch Handbook is an open-source book aimed at helping friends who want to use PyTorch for deep learning development and research to get started quickly. This is a GitHub open-source project created by zergtant, specifically providing systematic learning materials for PyTorch beginners.

Project Features

1. High Practicality

• Testing Guarantee: All PyTorch tutorials included are guaranteed to run successfully through testing.
• Version Synchronization: Due to PyTorch version iterations, the tutorial version will be consistent with the PyTorch version.
• Current Version: Current version 1.11

2. Open Source Transparency

• Citation Standards: Due to my limited level, I have referred to some online materials when writing this tutorial. I express my respect to them here, and I will attach the original address in each citation for your reference.
• Continuous Updates: Deep learning technology is developing rapidly, and PyTorch is constantly updating. I will gradually improve the relevant content.

3. Community Support

• QQ Group Communication: Group number: 760443051
• Multiple Communication Groups: There are already 6 communication groups, and the first 5 groups are full.
• Issue Feedback: Please directly submit issues if you have any problems.

Project Structure

Chapter 1: PyTorch Basics

• 1.1 Introduction to PyTorch: Introduces the basic concepts and features of PyTorch.
• 1.2 PyTorch Environment Setup: Detailed installation and configuration guide.
• 1.3 PyTorch Deep Learning: 60-Minute Blitz: Official quick start tutorial.
• 1.4 Introduction to Related Resources: Provides learning resources and reference materials.

Chapter 2: Neural Network Basics

• 2.3 Introduction to Neural Networks: Provided in Jupyter Notebook format.
• Note: This chapter may crash when opened locally with Microsoft Edge. Please use Chrome or Firefox to view it.

Chapter 3: Data Processing

• Contains examples and tutorials related to data processing.
• Provides actual dataset examples.

Chapter 4: Advanced Features

• Distributed Training: Using DistributedDataParallel in PyTorch for multi-GPU distributed model training.
• Data Transformation: Summary of common operations of transforms.
• Loss Function: Summary of PyTorch loss functions.
• Optimizer: Summary of PyTorch optimizers.

Technical Features

1. Multiple Format Support

• Jupyter Notebook: .ipynb format, supports interactive learning.
• Markdown Document: .md format, easy to read and understand.
• Python Script: Actual runnable code examples.

2. Practice-Oriented

• CIFAR-10 Tutorial: Contains a complete image classification project.
• Automatic Gradient Mechanism: Detailed explanation of the use of autograd.
• Practical Cases: Provides examples of multiple practical application scenarios.

3. Tool Support

• Conversion Script: The script directory contains scripts I wrote to convert ipynb to online versions and PDF files.
• Online Version: Supports online browsing and learning.
• Mirror Support: Domestic mirror, fast speed, not blocked: https://www.pytorch.wiki/

Learning Path

Beginner Recommended Path

1. Environment Setup: Start from Chapter 1 and complete the PyTorch installation.
2. Basic Concepts: Learn the basic concepts and operations of PyTorch.
3. Neural Networks: Understand the basic principles of neural networks.
4. Practical Projects: Deepen understanding through projects such as CIFAR-10.
5. Advanced Features: Learn distributed training and optimization techniques.

Advanced Learning

• In-depth study of various loss functions and optimizers.
• Master data preprocessing and transformation techniques.
• Learn multi-GPU distributed training.
• Participate in community discussions and question answering.

Instructions for Use

Get the Code

git clone https://github.com/zergtant/pytorch-handbook.git
cd pytorch-handbook

Running Environment

• Python 3.x
• PyTorch 1.11+
• Jupyter Notebook
• Related dependency packages

Browser Compatibility

• Recommended: Chrome, Firefox
• Not Recommended: Microsoft Edge (may cause crashes)

Contribution Guide

How to Contribute

1. Error Correction: Please directly submit issues or PRs to modify typos.
2. Version Attention: Please pay attention to the version when submitting PRs.
3. Issue Feedback: Report issues through GitHub Issues.

Community Interaction

• Join the QQ communication group to participate in discussions.
• Submit questions and suggestions on GitHub.
• Share learning experiences and insights.

Project Value

Learning Value

• Systematic: Provides a complete PyTorch learning path.
• Practical: All code has been tested and verified.
• Timely: Keeps up-to-date with PyTorch version updates.

Community Value

• Open Source Contribution: Provides high-quality resources for the PyTorch Chinese community.
• Knowledge Sharing: Promotes the popularization of deep learning technology.
• Collaboration Platform: Provides a platform for learners to communicate and discuss.

Future Development

Planned Improvements

• PDF Version: I haven't found a good way to generate PDF files yet. Friends who are familiar with this can contact me.
• Content Expansion: Continuously add new tutorials and cases.
• Version Update: Follow the new features of the PyTorch version.

Long-Term Goals

• Establish a more complete PyTorch Chinese learning system.
• Provide more practical projects and cases.
• Cultivate more PyTorch developers.

Summary

PyTorch Handbook is an excellent open-source learning resource, especially suitable for Chinese users to learn PyTorch. The project has a clear structure, practical content, and an active community, making it an ideal choice for deep learning beginners and advanced learners. By systematically studying this project, you can quickly master the core concepts and practical application skills of PyTorch.