Introduction to PyTorch-Tutorial-2nd
The "PyTorch Practical Tutorial (Second Edition)" project is a comprehensive guide that builds on the success of its predecessor, offering an enriched learning experience in the field of deep learning. This second edition, after five years since the first, has been meticulously crafted over four years to provide readers with a thorough understanding of deep learning applications through case studies and inference deployment frameworks. It aims to equip learners with the knowledge needed to succeed as deep learning engineers in the ever-evolving artificial intelligence landscape.
Overview
The project consists of an online book and accompanying open-source code, both freely accessible to learners. The content is organized into three main sections: Basics, Industry Applications, and Deployment.
Content Breakdown
Basics
The first section serves as an introduction to PyTorch, specifically tailored for beginners, non-traditional learners, and undergraduate students. It covers:
- Introduction to PyTorch and setting up the development environment.
- Insight into core modules such as data handling, models, optimization, and visualization.
- Building a foundational code structure using PyTorch knowledge to prepare for advanced topics.
Industry Applications
This section is designed for learners to apply their foundational knowledge in real-world scenarios, focusing on three main themes:
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Computer Vision (CV): This chapter explores essential tasks such as image classification, segmentation, detection, tracking, GAN and diffusion generation, image captioning, and retrieval, illustrating the practical applications of CV.
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Natural Language Processing (NLP): The NLP chapter dives into models like RNN, LSTM, Transformer, BERT, and GPT, with applications in text classification, machine translation, named entity recognition, Q&A, and text generation.
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Large Language Models (LLM): The LLM section covers deployment and code analysis of four major open-source models - Qwen, ChatGLM, Baichuan, and Yi - as well as a specialized industry application called GPT Academic for academic optimization.
Deployment
With a solid grasp on the tools and applications, this section focuses on deploying models to create valuable, user-friendly algorithm services. It includes:
- Understanding and using ONNX and TensorRT for model deployment and acceleration.
- Detailed exploration of model quantization concepts, including PTQ (Post-Training Quantization) and QAT (Quantization-Aware Training), using TensorRT.
Highlights of the Book
- Structured Learning: Guides the reader from basic to advanced topics systematically.
- Theory and Practice Integration: Combines detailed theoretical explanations and practical applications across multiple domains.
- Rich Practical Examples: Covers comprehensive real-world projects in computer vision, natural language processing, and large language models.
- Broad Scope: Encompasses essential areas like PyTorch basics, computer vision, NLP tasks, large language models, and deployment strategies.
- Wide Applicability: Suitable for AI self-learners, AI product managers, students, and multidisciplinary professionals.
Community and Licensing
To foster a collaborative learning environment, the project offers a QQ group for discussion and knowledge exchange. The work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License, promoting knowledge sharing while protecting intellectual property.
In conclusion, the "PyTorch Practical Tutorial (Second Edition)" is an invaluable resource for anyone looking to deepen their understanding of PyTorch and apply it effectively in various fields of deep learning. Whether you're a novice or an experienced professional, this project provides a pathway to mastering both foundational and advanced AI concepts.