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Books for machine learning, deep learning, math, NLP, CV, RL, etc

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README.md

Books for Machine Learning, Deep Learning, and related topics

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. Elements of Statistical Learning-2017.pdf
19. Foundations of Data Science-2018.pdf
20. Fundamentals of Deep Learning-2017.pdf
21. Gaussian Processes for Machine Learning-2006.pdf
22. Hands on Machine Learning with Scikit Learn and TensorFlow-En-2017.pdf
23. Hands on Machine Learning with Scikit Learn and TensorFlow-中文-机器学习实用指南-2017.pdf
24. Hands on Machine Learning with Scikit Learn Keras and TensorFlow 2nd Edition-2019.pdf
25. Introduction to Machine Learning with Python-2016.pdf
26. Introduction to Machine Learning-sencond-edition-EN-2010.pdf
27. Learning Generative Adversarial Networks-2017.pdf
28. Learning TensorFlow-2017.pdf
29. Machine Learning for OpenCV-2017.pdf
30. Machine Learning in Action-EN-2012.pdf
31. Machine Learning in Action-中文-2012.pdf
32. Machine Learning in Python-2015.pdf
33. Machine Learning with Python Scikit-Learn-2015.pdf
34. Machine Learning Yearning-Andrew Ng-2018.pdf
35. Machine Learning-A Probabilistic Perspective-2012.pdf
36. Mastering Feature Engineering-2016.pdf
37. Mastering Machine Learning with scikit-learn-2017.pdf
38. MATLAB Machine Learning by Michael Paluszek-2017.pdf
39. Pattern Recognition And Machine Learning _中文-马春鹏-2014.pdf
40. Pattern Recognition And Machine Learning-EN-2006.pdf
41. Practical Machine Learning with H2O-2016.pdf
42. Practical Machine Learning-A New Look at Anomaly Detection-2014.pdf
43. Pro Deep Learning with TensorFlow-2017.pdf
44. Python Machine Learning-2015.pdf
45. Python Real World Machine Learning — Prateek Joshi-2016.pdf
46. Tensorflow for Deep Learning Research-Stanford CS 20-2018
47. Tensorflow Machine Learning Cookbook-2017.pdf
48. Tensorflow实战Google深度学习框架-2017.pdf
49. 机器学习(西瓜书)_周志华-中文-2016.pdf
50. 深度学习入门之PyTorch-2017.pdf

1. An introduction to optimization-4th-edition-2013.pdf
2. Convex Optimization-2009.pdf
3. Introduction to Applied Linear Algebra-2018.pdf
4. Introduction to Linear Algebra-5th edition-2016.pdf
5. Mathematics and Computation-2018.pdf
6. Mathematics for Machine Learnin-2017.pdf
7. Mathematics for machine learning-2017.pdf
8. Mathematics for Machine Learning-2019
9. MIT18_657_Mathematics of Machine Learning-2015.pdf
10. The Matrix Cookbook-2012.pdf
11. 凸优化-中文版-2013.pdf
12. 数学分析教程-常庚哲_史济怀-上册-2003.pdf
13. 数学分析教程-常庚哲_史济怀-下册-2003.pdf
14. 最优化导论-第四版-2015.pdf
15. 贝叶斯网引论-张连文-2006.pdf
16. 高等代数学习指导书.丘维声.上册-2005.pdf
17. 高等代数学习指导书.丘维声·下册-2009.pdf
18. 高等代数（上）丘维声-2010.pdf
19. 高等代数（下）丘维声-2010.pdf

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

6. Reinforcement Learning Books

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

About

Books for machine learning, deep learning, math, NLP, CV, RL, etc

Источник

Building Machine Learning Systems with Python PDF Free

A Smarter Way to Learn Python in computer programming, python language and computer science book which shares the hacks to master coding. Mark Myers is the author of this impressive book. This book takes readers step by step to master python language. Python is the high-level coding language in the market. There are millions of job opportunities for the python developers and they are earning handsome pays. Mark takes the reader step by step to learn python and become an expert just in few weeks. First of all, make a habit of coding every day. Make a plan and stick with it. Take little breaks while coding as it helps you to make logical statements.

Hands-On Docker for Microservices with Python

Hands-On Docker for Microservices with Python is the python programming, web services and wen development guide for the students and professionals. Jaime Buelta is the author of this magnificent book. This guide is for software architects, engineers, and developers who are trying to switch from traditional approaches to making complex systems. It will provide them a simple way to develop complex multi-service systems through containers and microservices. There are no additional skills require to master the Docker if you already know Python language. Learn the different techniques to design, execute, move and plan the whole system.

Machine Learning for Cybersecurity Cookbook

Machine Learning for Cybersecurity Cookbook is the artificial intelligence, network programming, python programming and network security book which tells scientists how to apply modern AI to create powerful cybersecurity solutions. Emmanuel Tsukerman is the author of this fabulous book. This guide is helpful for security researchers and cybersecurity professionals who wanted to implement the latest techniques to enhance computer security. It shares the advanced machine learning techniques which are highly recommended for the data scientists. This book will show them how to experiment the AI techniques while staying on the domain of cybersecurity. It requires fundamental knowledge of python to master.

Twisted Network Programming Essentials

The “Twisted Network Programming Essentials: Event-driven Network Programming with Python, 2nd Edition” is extremely useful for getting a hands-on introduction to the framework. Jessica McKellar is the author of this programming book. Jessica is a software engineer from Cambridge, MA. She enjoys the Internet, networking, low-level systems engineering, and contributing to and helping other people contribute to open-source software. In this book, Jessica McKellar shares a guided tour of building fairly standard twisted applications.

Python Programming For Beginners In 2020

The “Python Programming For Beginners In 2020: Learn Python In 5 Days with Step-By-Step Guidance, Hands-On Exercises And Solution – Fun Tutorial For Novice Programmers (Coding Crash Course)” is a step by step guide book for the beginners. James Tudor is the author of this excellent book. In this book, you will learn how to write the first code with Python. James shares numerous examples and screenshots that will help the reader and engage from start to end of the page. If you are all about learning, then this is the perfect book you need. It is a very nicely organized thorough book with well-formed Python concepts.

Python for Everybody: Exploring Data in Python 3 PDF Free

Python for Everybody: Exploring Data in Python 3 is one of the best books ever written on development in python. Charles Severance is the author of this book. He teaches Informatics courses as a Clinical Associate Professor in the School of Information at the University of Michigan. Previously he was the Executive Director of the Sakai Foundation and the Chief Architect of the Sakai Project. In this book, he shares his programming experience, especially in Python. This book enables readers to think of the Python programming language as a tool to solve data problems that are beyond the capability of a spreadsheet. This book uses the Python 3 language and presents the Python concepts in a very easy and understandable way. It covers the basics well and then it goes on to explore real-world use cases. All the examples in the book are well explained. In summary, if you really want to get started with Python then we highly recommend you Python for Everybody by Dr. Charles Russell Severance. You can also Download Building Machine Learning Systems with Python PDF.

Источник

Building Machine Learning Systems with Python

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.

Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.

Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques.

Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.

Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.

Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.

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Источник