Deep Learning Book Project Details

Project Overview

The Deep Learning Book is a textbook resource designed to help students and practitioners enter the field of machine learning, particularly deep learning. The online version of the book is now complete and will continue to be available online for free.

Authors

The book is co-authored by three leading experts in the field of deep learning:

• Ian Goodfellow - Inventor of Generative Adversarial Networks (GANs), former Google Brain researcher
• Yoshua Bengio - 2018 Turing Award winner, one of the three deep learning pioneers
• Aaron Courville - Professor at the University of Montreal, deep learning research expert

Publication Information

@book{Goodfellow-et-al-2016, 
    title={Deep Learning}, 
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville}, 
    publisher={MIT Press}, 
    note={\url{http://www.deeplearningbook.org}}, 
    year={2016} 
}

• Publisher: MIT Press
• Publication Year: 2016
• ISBN: 978-0262035613

Project Features

1. Free Online Access

• The complete online version is permanently available for free
• The web version is in HTML format for easy online reading
• Supports direct printing from the browser (Chrome browser recommended)

2. Authority and Comprehensiveness

• This is currently the most comprehensive book on deep learning
• Covers the theoretical foundations, methodologies, and practical applications of deep learning
• Used as a textbook by many universities worldwide

3. Academic Standards

• Provides standard academic citation formats
• Contains complete mathematical symbols and notation systems
• Provides LaTeX template files for academic writing

Technical Features

Content Structure

• Theoretical Foundations: Covers mathematical foundations such as linear algebra, probability theory, and information theory
• Machine Learning Basics: Introduces traditional machine learning concepts
• Deep Learning Core: Explains core concepts such as neural networks and backpropagation in detail
• Practical Applications: Includes applications such as convolutional neural networks and recurrent neural networks

Technical Implementation

• Provides online reading in HTML format
• Supports the complete display of mathematical formulas
• Provides a complete symbol index and glossary

How to Obtain

Free Online Version

• Official Website: https://www.deeplearningbook.org/
• Completely free, no registration required

Print Version

• Print versions can be purchased through platforms such as Amazon
• Supports worldwide delivery

Academic Resources

• Provides LaTeX templates and symbol table downloads
• Supports academic citation and teaching use

Community and Support

Official Channels

• Official Mailing List: deeplearningbook Google Groups
• Feedback Email: feedback@deeplearningbook.org

Community Contributions

• Welcome error corrections and exercise suggestions
• Maintains a list of known issues
• Provides technical support and answers to questions

Usage Suggestions

Target Audience

• Machine learning beginners and practitioners
• Computer science and related major students
• Researchers and engineers

Learning Path

1. First master the necessary mathematical foundations (linear algebra, probability theory)
2. Learn basic machine learning concepts step by step
3. Study deep learning core technologies in depth
4. Consolidate theoretical knowledge with practical projects

Technical Requirements

• It is recommended to use the Chrome browser for the best reading experience
• Requires a certain amount of mathematical and programming foundation
• Can be combined with other practical resources for learning

Precautions

• Due to contract restrictions with MIT Press, electronic versions in easily copied formats such as PDF are not provided
• HTML format is used as a digital copyright protection measure
• Only minor corrections will be made, there will be no large-scale content updates
• Some browsers may have symbol display issues, it is recommended to use the latest version of the browser