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https://github.com/hao-lh/the-books-making-you-better
A list of time-lasting classic books, which not only help you figure out how it works, but also grasp when it works and why it works in that way.
https://github.com/hao-lh/the-books-making-you-better
bayesian-inference computer-architecture computer-vision deep-learning high-performance-computing linear-algebra machine-learning probabilistic-graphical-models reinforcement-learning statistical-learning
Last synced: 5 days ago
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A list of time-lasting classic books, which not only help you figure out how it works, but also grasp when it works and why it works in that way.
- Host: GitHub
- URL: https://github.com/hao-lh/the-books-making-you-better
- Owner: hao-lh
- Created: 2016-12-20T00:34:21.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-01-13T14:24:15.000Z (10 months ago)
- Last Synced: 2024-08-01T21:59:08.874Z (3 months ago)
- Topics: bayesian-inference, computer-architecture, computer-vision, deep-learning, high-performance-computing, linear-algebra, machine-learning, probabilistic-graphical-models, reinforcement-learning, statistical-learning
- Homepage:
- Size: 254 KB
- Stars: 174
- Watchers: 3
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Books Making You Better
## Contributing
Please feel free to send me [pull requests](https://github.com/hao-lh/books-making-you-better/pulls) or [email](mailto:[email protected]) to add links.
## Table of Contents
- [Books](#books)
- [Programming](#programming)
- [C/C++](#c-cpp)
- [Python](#python)
- [CUDA](#cuda)
- [Computer System](#computer-system)
- [Operating System](#operating-system)
- [System Design](#system-design)
- [Mathematical Foundations](#mathematical-foundations)
- [Linear Algebra](#linear-algebra)
- [Statistics](#statistics)
- [Algorithms](#algorithms)
- [Machine Learning and Deep Learning](#machine-learning-deep-learning)
- [Machine Learning](#machine-learning)
- [Deep Learning](#deep-learning)
- [Computer Vision](#computer-vision)
- [Probabilistic Graphic Model](#probabilistic-graphic-model)
- [Courses](#courses)
- [Machine Learning and Statistical Learning](#courses-machine-learning-statistical-learning)
- [Computer Systems](#courses-computer-systems)
- [Papers](#papers)
- [Deep Learning](#papers-deep-learning)
## Books
#### Programming
##### C/C++
* [The C++ Programming Language (2013,4th)](http://www.stroustrup.com/4th.html) - Bjarne Stroustrup
* [C++ Primer (2012,5th)](http://www.informit.com/store/c-plus-plus-primer-9780321714114) - Stanley B. Lippman
* [The C++ Standard Library: A Tutorial and Reference (2012,2nd)](http://www.josuttis.com/libbook/) - Nicolai M. Josuttis
* [C++ Templates: The Complete Guide (2017,2nd)](https://www.amazon.com/C-Templates-Complete-Guide-2nd/dp/0321714121) - David Vandevoorde
* [Effective C++ (2005,3rd)](https://www.amazon.com/gp/product/0321334876) - Scott Meyers
* [More Effective C++ (1996)](https://www.amazon.com/gp/product/020163371X) - Scott Meyers
* [Effective STL (2001)](https://www.amazon.com/gp/product/0201749629) - Scott Meyers
* [Effective Modern C++ (2014)](https://www.amazon.com/gp/product/1491903996) - Scott Meyers
* [Inside the C++ Object Model (1996)](https://www.amazon.com/Inside-Object-Model-Stanley-Lippman/dp/0201834545) - Stanley B. Lippman
* [Expert C Programming: Deep C Secrets (1994)](https://www.amazon.com/Expert-Programming-Peter-van-Linden/dp/0131774298) - Peter Van Der Linden
* [Understanding and Using C Pointers (2013)](https://www.amazon.com/Understanding-Using-Pointers-Techniques-Management/dp/1449344186) - Richard M Reese
* [21st Century C: C Tips from the New School (2014,2nd)](https://www.amazon.com/21st-Century-Tips-New-School/dp/1491903899) - Ben Klemens
* [C++ Concurrency in Action (2019,2nd)](https://www.manning.com/books/c-plus-plus-concurrency-in-action-second-edition) - Anthony Williams
##### Python
* [Learning Python (2013,5th)](https://learning-python.com/about-lp.html) - Mark Lutz
* [Python Cookbook (2013,3rd)](http://www.dabeaz.com/cookbook.html) - Brian Jones and David Beazley
* [Fluent Python: Clear, Concise, and Effective Programming (2022,2nd)](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1492056359) - Luciano Ramalho
##### CUDA
* [CUDA by Example: An Introduction to General-Purpose GPU Programming (2010)](https://www.amazon.com/CUDA-Example-Introduction-General-Purpose-Programming/dp/0131387685/) - Jason Sanders
* [Professional CUDA C Programming (2014)](https://www.amazon.com/Professional-CUDA-Programming-John-Cheng/dp/1118739329/) - John Cheng
* [Programming Massively Parallel Processors: A Hands-on Approach (2016,3rd)](https://www.amazon.com/Programming-Massively-Parallel-Processors-Hands/dp/0128119861) - 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)](https://www.mheducation.com/highered/product/introduction-computing-systems-bits-gates-c-c-beyond-patt-patel/M9781260150537.html) - Yale N. Patt and Sanjay J. Patel
* [Computer Systems: A Programmer's Perspective (2015,3rd)](http://www.csapp.cs.cmu.edu) [[videos]](https://www.youtube.com/playlist?list=PLbY-cFJNzq7z_tQGq-rxtq_n2QQDf5vnM)[[slides]](http://www.cs.cmu.edu/afs/cs/academic/class/15213-f15/www/schedule.html) - Randal E. Bryant and David R. O'Hallaron
* [Operating Systems: Three Easy Pieces (2018)](http://pages.cs.wisc.edu/~remzi/OSTEP/) [[errata]](http://pages.cs.wisc.edu/~remzi/OSTEP/combined.html) - Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau
* [Operating Systems: Principles and Practice (2014,2nd)](http://ospp.cs.washington.edu) - Thomas Anderson and Michael Dahlin
* [The Linux Programming Interface (2010)](http://www.man7.org/tlpi/) - Michael Kerrisk
* [Computer Architecture: A Quantitative Approach (2017,6th)](https://www.amazon.com/Computer-Architecture-Quantitative-Approach-Kaufmann/dp/0128119055) - John Hennessy and David Patterson
##### System Design
* [Designing Data-Intensive Applications (2017)](https://martin.kleppmann.com/2017/03/27/designing-data-intensive-applications.html) [[About]](https://dataintensive.net/)[[Errata]](https://www.oreilly.com/catalog/errata.csp?isbn=0636920032175) - Martin Kleppmann
#### Mathematical Foundations
##### Linear Algebra
* [Linear Algebra and Its Applications (2016,5th)](https://www.pearson.com/us/higher-education/program/Lay-Linear-Algebra-and-Its-Applications-plus-New-My-Lab-Math-with-Pearson-e-Text-Access-Card-Package-5th-Edition/PGM2547338.html?tab=resources) - David C. Lay
* [Introduction to Linear Algebra (2016,5th)](http://math.mit.edu/~gs/linearalgebra/) - Gilbert Strang
* [Linear Algebra Done Right (2015,3rd)](http://linear.axler.net) - Sheldon Axler
* [Linear Algebra and Geometry (2013)](https://link.springer.com/book/10.1007/978-3-642-30994-6) - Igor R. Shafarevich and Alexey O. Remizov
##### Statistics
* [Probability Theory: The Logic of Science (2003)](http://www.cambridge.org/9780521592710) - E. T. Jaynes and G. Larry Bretthorst
* [Probability and Statistics (2011,4th)](https://www.amazon.com/Probability-Statistics-4th-Morris-DeGroot/dp/0321500466) - Morris H. DeGroot
* [Statistical Inference (2001,2nd)](https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126) - George Casella
##### Algorithms
* [Algorithms (2011,4th)](https://algs4.cs.princeton.edu/home/) - Robert Sedgewick and Kevin Wayne
* [The Algorithm Design Manual (2020,3rd)](http://www.algorist.com) [[errata]](https://www3.cs.stonybrook.edu/~skiena/algorist/book/errata-adm3) - Steven Skiena
#### Machine Learning and Deep Learning
##### Machine Learning
* [An Introduction to Statistical Learning (2013)](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
* [Pattern Recognition and Machine Learning (2007)](http://research.microsoft.com/en-us/um/people/cmbishop/prml/index.htm) [[Python](https://github.com/ctgk/PRML)/[Matlab](https://github.com/PRML/PRMLT)/[Solution](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/prml-web-sol-2009-09-08.pdf)/[Manual](https://github.com/zhengqigao/PRML-Solution-Manual/blob/master/PRML_Solution_Manual.pdf)] - Christopher M. Bishop
* [Machine Learning: a Probabilistic Perspective (2012)](https://www.cs.ubc.ca/~murphyk/MLbook/) [[code](https://github.com/probml/pmtk3)] - Kevin Patrick Murphy
* [Probabilistic Machine Learning: An Introduction (2021)](https://probml.github.io/pml-book/book1.html) [[code](https://github.com/probml/pyprobml)] - Kevin Patrick Murphy
* [Probabilistic Machine Learning: Advanced Topics (2022)](https://probml.github.io/pml-book/book2.html) [[code](https://github.com/probml/pyprobml)] - Kevin Patrick Murphy
* [The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009,2nd)](https://web.stanford.edu/~hastie/ElemStatLearn/) - Trevor Hastie, Robert Tibshirani and Jerome Friedman
* [Linear Algebra and Optimization for Machine Learning: A Textbook](https://charuaggarwal.net/) - Charu C. Aggarwal
##### Deep Learning
* [Grokking Deep Learning (2019)](https://www.manning.com/books/grokking-deep-learning) - Andrew W. Trask
* [Deep Learning with Python (2017)](https://www.manning.com/books/deep-learning-with-python) [[code](https://github.com/fchollet/deep-learning-with-python-notebooks)] - François Chollet
* [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019,2nd)](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) [[code](https://github.com/ageron/handson-ml2)] - Aurélien Géron
* [Neural Networks and Deep Learning: A Textbook (2018)](http://www.charuaggarwal.net/neural.htm) - Charu C. Aggarwal
* [Deep Learning (2016)](http://www.deeplearningbook.org) - Ian Goodfellow, Yoshua Bengio and Aaron Courville
* [Generative Deep Learning (2019)](https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/) - David Foster
##### Computer Vision
* [Multiple View Geometry in Computer Vision (2004,2nd)](http://www.robots.ox.ac.uk/~vgg/hzbook/) - Richard Hartley and Andrew Zisserman
##### Probabilistic Graphic Model
* [Probabilistic Graphical Models: Principles and Techniques (2009)](http://pgm.stanford.edu/) - Daphne Koller and Nir Friedman
## Courses
#### Machine Learning and Statistical Learning
* [Machine Learning](https://www.coursera.org/course/ml) - Andrew Ng (Stanford University)
* [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu) - Fei-Fei Li (Stanford University)
* [CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/) - Chris Manning (Stanford University)
* [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) - [deeplearning.ai](https://www.deeplearning.ai)
#### Computer Systems
* [The Missing Semester of Your CS Education (2020)](https://missing.csail.mit.edu/) - Anish, Jon, and Jose
## Papers
#### Deep Learning
* He, Kaiming, et al. "[Deep residual learning for image recognition](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html)." Proceedings of the IEEE conference on computer vision and pattern recognition. *2016*.
* Vaswani, Ashish, et al. "[Attention is all you need](https://arxiv.org/abs/1706.03762)." Advances in neural information processing systems 30 (*2017*).
## Star History
[](https://star-history.com/#hao-lh/the-road-to-computer-vision&Date)
## Licenses
License
[](http://creativecommons.org/publicdomain/zero/1.0/)
To the extent possible under law, [Hao](mailto:[email protected]) has waived all copyright and related or neighboring rights to this work.