Books Making You Better

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Table of Contents

• Books
• Courses

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 (2015) - 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 (2018,6th) - 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.