yolo2h/books-making-you-better

A list of time-lasting classic books, which could not only help you figure out how it works, but also grasp when it works and why it works in that way.

Books Making You Better

Contributing

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Table of Contents

• Books
  • Programming
    • C/C++
    • Python
    • CUDA
  • Computer System
    • Operating System
    • System Design
  • Mathematical Foundations
    • Linear Algebra
    • Statistics
    • Algorithms
  • Machine Learning and Deep Learning
    • Machine Learning
    • Deep Learning
    • Computer Vision
    • Probabilistic Graphic Model
• Courses
  • Machine Learning and Statistical Learning
  • Computer Systems

Books

Programming

C/C++

• The C++ Programming Language (2013,4th) - Bjarne Stroustrup
• C++ Primer (2012,5th) - Stanley B. Lippman
• The C++ Standard Library: A Tutorial and Reference (2012,2nd) - Nicolai M. Josuttis
• C++ Templates: The Complete Guide (2017,2nd) - David Vandevoorde
• Effective C++ (2005,3rd) - Scott Meyers
• More Effective C++ (1996) - Scott Meyers
• Effective STL (2001) - Scott Meyers
• Effective Modern C++ (2014) - Scott Meyers
• Inside the C++ Object Model (1996) - Stanley B. Lippman
• Expert C Programming: Deep C Secrets (1994) - Peter Van Der Linden
• Understanding and Using C Pointers (2013) - Richard M Reese
• 21st Century C: C Tips from the New School (2014,2nd) - Ben Klemens
• C++ Concurrency in Action (2019,2nd) - Anthony Williams

Python

• Learning Python (2013,5th) - Mark Lutz
• Python Cookbook (2013,3rd) - Brian Jones and David Beazley
• Fluent Python: Clear, Concise, and Effective Programming (2022,2nd) - Luciano Ramalho

CUDA

• CUDA by Example: An Introduction to General-Purpose GPU Programming (2010) - Jason Sanders
• Professional CUDA C Programming (2014) - John Cheng
• Programming Massively Parallel Processors: A Hands-on Approach (2016,3rd) - David B. Kirk and Wen-mei W. Hwu

Computer System

Operating System

• Introduction to Computing Systems: From Bits & Gates to C/C++ & Beyond (2020,3rd) - Yale N. Patt and Sanjay J. Patel
• Computer Systems: A Programmer's Perspective (2015,3rd) [videos][slides] - Randal E. Bryant and David R. O'Hallaron
• Operating Systems: Three Easy Pieces (2018) [errata] - Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau
• Operating Systems: Principles and Practice (2014,2nd) - Thomas Anderson and Michael Dahlin
• The Linux Programming Interface (2010) - Michael Kerrisk
• Computer Architecture: A Quantitative Approach (2017,6th) - John Hennessy and David Patterson

System Design

• Designing Data-Intensive Applications (2017) [About][Errata] - Martin Kleppmann

Mathematical Foundations

Linear Algebra

• Linear Algebra and Its Applications (2016,5th) - David C. Lay
• Introduction to Linear Algebra (2016,5th) - Gilbert Strang
• Linear Algebra Done Right (2015,3rd) - Sheldon Axler
• Linear Algebra and Geometry (2013) - Igor R. Shafarevich and Alexey O. Remizov

Statistics

• Probability Theory: The Logic of Science (2003) - E. T. Jaynes and G. Larry Bretthorst
• Probability and Statistics (2011,4th) - Morris H. DeGroot
• Statistical Inference (2001,2nd) - George Casella

Algorithms

• Algorithms (2011,4th) - Robert Sedgewick and Kevin Wayne
• The Algorithm Design Manual (2020,3rd) [errata] - Steven Skiena

Machine Learning and Deep Learning

Machine Learning

• An Introduction to Statistical Learning (2013) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• Pattern Recognition and Machine Learning (2007) [Python/Matlab/Solution/Manual] - Christopher M. Bishop
• Machine Learning: a Probabilistic Perspective (2012) [code] - Kevin Patrick Murphy
• Probabilistic Machine Learning: An Introduction (2021) [code] - Kevin Patrick Murphy
• Probabilistic Machine Learning: Advanced Topics (2022) [code] - Kevin Patrick Murphy
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009,2nd) - Trevor Hastie, Robert Tibshirani and Jerome Friedman
• Linear Algebra and Optimization for Machine Learning: A Textbook - Charu C. Aggarwal

Deep Learning

• Grokking Deep Learning (2019) - Andrew W. Trask
• Deep Learning with Python (2017) [code] - François Chollet
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019,2nd) [code] - Aurélien Géron
• Neural Networks and Deep Learning: A Textbook (2018) - Charu C. Aggarwal
• Deep Learning (2016) - Ian Goodfellow, Yoshua Bengio and Aaron Courville
• Generative Deep Learning (2019) - David Foster

Computer Vision

• Multiple View Geometry in Computer Vision (2004,2nd) - Richard Hartley and Andrew Zisserman

Probabilistic Graphic Model

• Probabilistic Graphical Models: Principles and Techniques (2009) - Daphne Koller and Nir Friedman

Courses

Machine Learning and Statistical Learning

• Machine Learning - Andrew Ng (Stanford University)
• CS231n: Convolutional Neural Networks for Visual Recognition - Fei-Fei Li (Stanford University)
• CS224n: Natural Language Processing with Deep Learning - Chris Manning (Stanford University)
• Deep Learning Specialization - deeplearning.ai

Computer Systems

• The Missing Semester of Your CS Education (2020) - Anish, Jon, and Jose

Licenses

License

To the extent possible under law, Hao has waived all copyright and related or neighboring rights to this work.