These four books are really good ！ We all know that machine learning now 、 There are too many materials for deep learning , In the face of massive resources , Often fall into “ Do not know how to start ” The perplexity of leaving the country . And not all books are good resources , It's not worth the loss to waste a lot of time .
Recommend these good books and make a brief introduction ：
Recommend index ：*****
Since its publication, this book has received many favorable comments , the reason being that Keras The book written by the author , So the book basically revolves around Keras Talk about the realization of deep learning , from CNN,RNN To GAN etc. , Partial introduction , But it also carries many authors' Thoughts on the integrity of deep learning . This is a practical book , Teach you how to use Keras Fast implementation of deep learning classic project . Finish the book , It's basically right Keras And deep learning actual combat has a relatively preliminary grasp of .
Source code of this book GitHub Address ：
https://github.com/fchollet/deep-learning-with-python-notebooks
Recommend index ：*****
This book uses Scikit-Learn and TensorFlow, Explain machine learning and deep learning respectively , And each chapter is equipped with operation code . Another point is to explain how to publish machine learning models to Web application . The whole knowledge system is relatively more perfect , It's a comprehensive machine learning book .
Source code of this book GitHub Address ：
https://github.com/rasbt/python-machine-learning-book-2nd-edition
Recommend index ：*****
This book is translated into Chinese 《Scikit-Learn And TensorFlow Practical guide to machine learning 》. The biggest feature of this book is, in theory, its conciseness , The book basically does not have too many complicated mathematical formula derivation , The language is easy to understand , It's easy to understand 、 I can see it . The original book gives consideration to both theory and practice , Is a very suitable for beginners and practical machine learning books .
Source code of this book GitHub Address ：
https://github.com/ageron/handson-ml
Recommend index ：*****
also called “ Flower Book ”. The book is written by three big men Ian Goodfellow、Yoshua Bengio and Aaron Courville writing , It's a classic textbook for in-depth learning . I believe that most of the people who go deep into this book know ！
Resource acquisition method ： official account 【 The computer vision Alliance 】 The background to reply ：9002, Electronic version available
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