└── README.md /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Books Making You Better 3 | 4 | ![Nerd-Geek-Reading-Book](https://user-images.githubusercontent.com/14138581/54471371-e750ca00-47f2-11e9-8f42-e3f04829aedd.jpg) 5 | 6 | 7 | ## Contributing 8 | Please feel free to send me [pull requests](https://github.com/hao-lh/books-making-you-better/pulls) or [email](mailto:hao.omni@gmail.com) to add links. 9 | 10 | ## Table of Contents 11 | 12 | - [Books](#books) 13 | - [Programming](#programming) 14 | - [C/C++](#c-cpp) 15 | - [Python](#python) 16 | - [CUDA](#cuda) 17 | - [Computer System](#computer-system) 18 | - [Operating System](#operating-system) 19 | - [System Design](#system-design) 20 | - [Mathematical Foundations](#mathematical-foundations) 21 | - [Linear Algebra](#linear-algebra) 22 | - [Statistics](#statistics) 23 | - [Algorithms](#algorithms) 24 | - [Machine Learning and Deep Learning](#machine-learning-deep-learning) 25 | - [Machine Learning](#machine-learning) 26 | - [Deep Learning](#deep-learning) 27 | - [Computer Vision](#computer-vision) 28 | - [Probabilistic Graphic Model](#probabilistic-graphic-model) 29 | - [Courses](#courses) 30 | - [Machine Learning and Statistical Learning](#courses-machine-learning-statistical-learning) 31 | - [Computer Systems](#courses-computer-systems) 32 | - [Papers](#papers) 33 | - [Deep Learning](#papers-deep-learning) 34 | 35 | 36 | 37 | ## Books 38 | #### Programming 39 | ##### C/C++ 40 | * [The C++ Programming Language (2013,4th)](http://www.stroustrup.com/4th.html) - Bjarne Stroustrup 41 | * [C++ Primer (2012,5th)](http://www.informit.com/store/c-plus-plus-primer-9780321714114) - Stanley B. Lippman 42 | * [The C++ Standard Library: A Tutorial and Reference (2012,2nd)](http://www.josuttis.com/libbook/) - Nicolai M. Josuttis 43 | * [C++ Templates: The Complete Guide (2017,2nd)](https://www.amazon.com/C-Templates-Complete-Guide-2nd/dp/0321714121) - David Vandevoorde 44 | * [Effective C++ (2005,3rd)](https://www.amazon.com/gp/product/0321334876) - Scott Meyers 45 | * [More Effective C++ (1996)](https://www.amazon.com/gp/product/020163371X) - Scott Meyers 46 | * [Effective STL (2001)](https://www.amazon.com/gp/product/0201749629) - Scott Meyers 47 | * [Effective Modern C++ (2014)](https://www.amazon.com/gp/product/1491903996) - Scott Meyers 48 | * [Inside the C++ Object Model (1996)](https://www.amazon.com/Inside-Object-Model-Stanley-Lippman/dp/0201834545) - Stanley B. Lippman 49 | * [Expert C Programming: Deep C Secrets (1994)](https://www.amazon.com/Expert-Programming-Peter-van-Linden/dp/0131774298) - Peter Van Der Linden 50 | * [Understanding and Using C Pointers (2013)](https://www.amazon.com/Understanding-Using-Pointers-Techniques-Management/dp/1449344186) - Richard M Reese 51 | * [21st Century C: C Tips from the New School (2014,2nd)](https://www.amazon.com/21st-Century-Tips-New-School/dp/1491903899) - Ben Klemens 52 | * [C++ Concurrency in Action (2019,2nd)](https://www.manning.com/books/c-plus-plus-concurrency-in-action-second-edition) - Anthony Williams 53 | ##### Python 54 | * [Learning Python (2013,5th)](https://learning-python.com/about-lp.html) - Mark Lutz 55 | * [Python Cookbook (2013,3rd)](http://www.dabeaz.com/cookbook.html) - Brian Jones and David Beazley 56 | * [Fluent Python: Clear, Concise, and Effective Programming (2022,2nd)](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1492056359) - Luciano Ramalho 57 | ##### CUDA 58 | * [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 59 | * [Professional CUDA C Programming (2014)](https://www.amazon.com/Professional-CUDA-Programming-John-Cheng/dp/1118739329/) - John Cheng 60 | * [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 61 | #### Computer System 62 | ##### Operating System 63 | * [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 64 | * [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 65 | * [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 66 | * [Operating Systems: Principles and Practice (2014,2nd)](http://ospp.cs.washington.edu) - Thomas Anderson and Michael Dahlin 67 | * [The Linux Programming Interface (2010)](http://www.man7.org/tlpi/) - Michael Kerrisk 68 | * [Computer Architecture: A Quantitative Approach (2017,6th)](https://www.amazon.com/Computer-Architecture-Quantitative-Approach-Kaufmann/dp/0128119055) - John Hennessy and David Patterson 69 | ##### System Design 70 | * [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 71 | #### Mathematical Foundations 72 | ##### Linear Algebra 73 | * [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 74 | * [Introduction to Linear Algebra (2016,5th)](http://math.mit.edu/~gs/linearalgebra/) - Gilbert Strang 75 | * [Linear Algebra Done Right (2015,3rd)](http://linear.axler.net) - Sheldon Axler 76 | * [Linear Algebra and Geometry (2013)](https://link.springer.com/book/10.1007/978-3-642-30994-6) - Igor R. Shafarevich and Alexey O. Remizov 77 | ##### Statistics 78 | * [Probability Theory: The Logic of Science (2003)](http://www.cambridge.org/9780521592710) - E. T. Jaynes and G. Larry Bretthorst 79 | * [Probability and Statistics (2011,4th)](https://www.amazon.com/Probability-Statistics-4th-Morris-DeGroot/dp/0321500466) - Morris H. DeGroot 80 | * [Statistical Inference (2001,2nd)](https://www.amazon.com/Statistical-Inference-George-Casella/dp/0534243126) - George Casella 81 | ##### Algorithms 82 | * [Algorithms (2011,4th)](https://algs4.cs.princeton.edu/home/) - Robert Sedgewick and Kevin Wayne 83 | * [The Algorithm Design Manual (2020,3rd)](http://www.algorist.com) [[errata]](https://www3.cs.stonybrook.edu/~skiena/algorist/book/errata-adm3) - Steven Skiena 84 | #### Machine Learning and Deep Learning 85 | ##### Machine Learning 86 | * [An Introduction to Statistical Learning (2013)](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani 87 | * [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 88 | * [Machine Learning: a Probabilistic Perspective (2012)](https://www.cs.ubc.ca/~murphyk/MLbook/) [[code](https://github.com/probml/pmtk3)] - Kevin Patrick Murphy 89 | * [Probabilistic Machine Learning: An Introduction (2021)](https://probml.github.io/pml-book/book1.html) [[code](https://github.com/probml/pyprobml)] - Kevin Patrick Murphy 90 | * [Probabilistic Machine Learning: Advanced Topics (2022)](https://probml.github.io/pml-book/book2.html) [[code](https://github.com/probml/pyprobml)] - Kevin Patrick Murphy 91 | * [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 92 | * [Linear Algebra and Optimization for Machine Learning: A Textbook](https://charuaggarwal.net/) - Charu C. Aggarwal 93 | ##### Deep Learning 94 | * [Grokking Deep Learning (2019)](https://www.manning.com/books/grokking-deep-learning) - Andrew W. Trask 95 | * [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 96 | * [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 97 | * [Neural Networks and Deep Learning: A Textbook (2018)](http://www.charuaggarwal.net/neural.htm) - Charu C. Aggarwal 98 | * [Deep Learning (2016)](http://www.deeplearningbook.org) - Ian Goodfellow, Yoshua Bengio and Aaron Courville 99 | * [Generative Deep Learning (2019)](https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/) - David Foster 100 | ##### Computer Vision 101 | * [Multiple View Geometry in Computer Vision (2004,2nd)](http://www.robots.ox.ac.uk/~vgg/hzbook/) - Richard Hartley and Andrew Zisserman 102 | ##### Probabilistic Graphic Model 103 | * [Probabilistic Graphical Models: Principles and Techniques (2009)](http://pgm.stanford.edu/) - Daphne Koller and Nir Friedman 104 | 105 | ## Courses 106 | #### Machine Learning and Statistical Learning 107 | * [Machine Learning](https://www.coursera.org/course/ml) - Andrew Ng (Stanford University) 108 | * [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu) - Fei-Fei Li (Stanford University) 109 | * [CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/) - Chris Manning (Stanford University) 110 | * [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) - [deeplearning.ai](https://www.deeplearning.ai) 111 | #### Computer Systems 112 | * [The Missing Semester of Your CS Education (2020)](https://missing.csail.mit.edu/) - Anish, Jon, and Jose 113 | 114 | 115 | ## Papers 116 | #### Deep Learning 117 | * 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*. 118 | * Vaswani, Ashish, et al. "[Attention is all you need](https://arxiv.org/abs/1706.03762)." Advances in neural information processing systems 30 (*2017*). 119 | 120 | 121 | ## Star History 122 | [![Star History Chart](https://api.star-history.com/svg?repos=hao-lh/the-road-to-computer-vision&type=Date)](https://star-history.com/#hao-lh/the-road-to-computer-vision&Date) 123 | 124 | 125 | ## Licenses 126 | License 127 | 128 | [![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/) 129 | 130 | To the extent possible under law, [Hao](mailto:hao.omni@gmail.com) has waived all copyright and related or neighboring rights to this work. 131 | --------------------------------------------------------------------------------