分享机器学习/深度学习书籍的GitHub

2024年3月22日 164次阅读 来源: 凯恩博

分享一个关于机器学习、深度学习书籍的GitHub，所有书籍以PDF的形式呈现。

建议认可书籍的朋友购买纸质书，以支持原作者。

比如其中的西瓜书、花书是非常不错的，建议购买纸质书，以便随时查阅 :)

GitHub地址：

https://github.com/loveunk/Deep-learning-books​ github.com

包括的书籍有：

1. Machine Leaning and Deep Learning

  1. A First Course in Machine Learning-2012.pdf
  2. AutoML Machine Learning-Methods, Systems, Challenges-2018.pdf
  3. Building Machine Learning Systems with Python-2nd Edition-2015.pdf
  4. Data Mining, Inference, and Prediction-2017.pdf
  5. Data Science from Scratch- First Principles with Python-2015.pdf
  6. Deep Learning with Keras-2017.pdf
  7. Deep Learning with Python A Hands-on Introduction-2017.pdf
  8. Deep Learning With Python-Develop Deep Learning Models on Theano and TensorFlow Using Keras-2017.pdf
  9. Deep Learning with Python-Francois_Chollet-En-2018.pdf
  10. Deep Learning with Python-Francois_Chollet-中文-Python深度学习-2018.pdf
  11. Deep Learning with Tensorflow-2017.pdf
  12. Deep Learning-EPFL EE559-2019
  13. Deep Learning-Josh Patterson & Adam Gibson-2017.pdf
  14. Deep_Learning-Ian_Goodfellow-En-2016.pdf
  15. Deep_Learning-Ian_Goodfellow-中文-2017.pdf
  16. Deep_Learning-台大李宏毅-En-2016.pdf
  17. Designing Machine Learning Systems with Python-2016.pdf
  18. Foundations of Data Science-2018.pdf
  19. Fundamentals of Deep Learning-2017.pdf
  20. Gaussian Processes for Machine Learning-2006.pdf
  21. Hands on Machine Learning with Scikit Learn and TensorFlow-En-2017.pdf
  22. Hands on Machine Learning with Scikit Learn and TensorFlow-中文-机器学习实用指南-2017.pdf
  23. Hands on Machine Learning with Scikit Learn Keras and TensorFlow 2nd Edition-2019.pdf
  24. Introduction to Machine Learning with Python-2016.pdf
  25. Introduction to Machine Learning-sencond-edition-EN-2010.pdf
  26. Learning Generative Adversarial Networks-2017.pdf
  27. Learning TensorFlow-2017.pdf
  28. Machine Learning for OpenCV-2017.pdf
  29. Machine Learning in Action-EN-2012.pdf
  30. Machine Learning in Action-中文-2012.pdf
  31. Machine Learning in Python-2015.pdf
  32. Machine Learning with Python Scikit-Learn-2015.pdf
  33. Machine Learning Yearning-Andrew Ng-2018.pdf
  34. Machine Learning-A Probabilistic Perspective-2012.pdf
  35. Mastering Feature Engineering-2016.pdf
  36. Mastering Machine Learning with scikit-learn-2017.pdf
  37. MATLAB Machine Learning by Michael Paluszek-2017.pdf
  38. Pattern Recognition And Machine Learning _中文-马春鹏-2014.pdf
  39. Pattern Recognition And Machine Learning-EN-2006.pdf
  40. Practical Machine Learning with H2O-2016.pdf
  41. Practical Machine Learning-A New Look at Anomaly Detection-2014.pdf
  42. Pro Deep Learning with TensorFlow-2017.pdf
  43. Python Machine Learning-2015.pdf
  44. Python Real World Machine Learning – Prateek Joshi-2016.pdf
  45. Tensorflow for Deep Learning Research-Stanford CS 20-2018
  46. Tensorflow Machine Learning Cookbook-2017.pdf
  47. Tensorflow实战Google深度学习框架-2017.pdf
  48. 机器学习(西瓜书)_周志华-中文-2016.pdf
  49. 深度学习入门之PyTorch-2017.pdf

2. Python Books

  1. Learn Python The Hard Way 3rd Edition-2014.pdf
  2. Learning PySpark-2017.pdf
  3. Python Data Analytics-2015.pdf
  4. SciPy and NumPy-2012.pdf
  5. Scipy Lecture Notes-2015.pdf
  6. Understanding GIL-2010.pdf

3. Math Books

  1. Introduction to Applied Linear Algebra-2018.pdf
  2. Introduction to Linear Algebra-5th edition-2016.pdf
  3. Mathematics and Computation-2018.pdf
  4. Mathematics for Machine Learnin-2017.pdf
  5. Mathematics for machine learning-2017.pdf
  6. Mathematics for Machine Learning-2019
  7. MIT18_657_Mathematics of Machine Learning-2015.pdf
  8. The Matrix Cookbook-2012.pdf
  9. 凸优化-中文版-2013.pdf
  10. 数学分析教程-常庚哲_史济怀-上册-2003.pdf
  11. 数学分析教程-常庚哲_史济怀-下册-2003.pdf
  12. 贝叶斯网引论-张连文-2006.pdf
  13. 高等代数学习指导书.丘维声.上册-2005.pdf
  14. 高等代数学习指导书.丘维声·下册-2009.pdf
  15. 高等代数（上）丘维声-2010.pdf
  16. 高等代数（下）丘维声-2010.pdf

4. NLP Books

  1. Applied Text Analysis with Python-2016.pdf
  2. Natural Language Processing with Python-2009.pdf
  3. Natural Language Processing with Python.pdf
  4. Natural Language Processing-2018.pdf
  5. Natural Language Understanding with Distributed Representation-2017.pdf
  6. Neural Transfer Learning for Natural Language Processing-Sebastian Ruder-2019.pdf
  7. NLTK Essentials-2015.pdf
  8. oxford-cs-deepnlp-2017
  9. Text Analytics with Python A Practical Real-World Approach to Gaining Actionable Insights from your Data-2016.pdf
  10. The Text Mining HandBook-2007.pdf
  11. 自然语言处理综论-2005.pdf

5. Computer Vision (CV) Book

  1. Learning Image Processing with OpenCV-2015.pdf

6. Reinforcement Learning Books

  1. An Introduction to Deep Reinforcement Learning-2018.pdf
  2. Dissecting Reinforcement Learning-2016
  3. Reinforce Learning-An introduction, 2nd edition-2018.pdf

7. Speech Processing

  1. Automatic Speech Recognition-解析深度学习语音识别实践-中文-2016.pdf
  2. Automatic Speech Recognition_A Deep Learning Approach-2015.pdf
  3. Spoken Language Processing-2001.pdf
  4. 离散时间信号处理_第3版_中文-2015.pdf
  5. 离散时间语音信号处理：原理与应用-中文-2004.pdf

8. Cheatsheets

  1. bokeh-cheatsheet.pdf
  2. cheatsheet-deep-learning.pdf
  3. cheatsheet-machine-learning-tips-and-tricks.pdf
  4. cheatsheet-supervised-learning.pdf
  5. cheatsheet-unsupervised-learning.pdf
  6. keras-cheatsheet.pdf
  7. linearAlgebra-cheatsheet.pdf
  8. matplotlib-cheatsheet.pdf
  9. notebook-cheatsheet.pdf
  10. numpy-cheatsheet.pdf
  11. pandas-cheatsheet.pdf
  12. refresher-algebra-calculus.pdf
  13. refresher-probabilities-statistics.pdf
  14. super-cheatsheet-machine-learning.pdf

再强调一下：

建议认可书籍质量的朋友购买纸质书，以支持原作者。

比如其中的西瓜书、花书是非常不错的，建议购买纸质书，以便随时查阅

    原文作者：凯恩博
    原文地址: https://zhuanlan.zhihu.com/p/60710810
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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