Back at this time last year, we published a great manual On Machine Learning called Hands-on Machine Learning with SciKit-Learn & TensorFlow. Scikit-learn and TensorFlow machine Learning Practical Guide \

The biggest feature of this book is that it is concise and comprehensive in theory. There are basically no complicated mathematical formulas in the book, and the language is easy to understand and read. This red stone is also found to be lacking in many textbooks. Coco Teacher also recommended this book. The best practical guide to machine learning in 2018 is here!

Blockbuster!

A year later, this awesome practical guide to machine learning has finally been published in its second edition: Hands-on Machine Learning with SciKit-Learn, Keras, and TensorFlow, 2nd Edition Scikit-learn, Keras, and TensorFlow: A Practical Guide to Machine Learning (2nd edition), is available on Amazon in the US, but not yet in China. Please look at the cover: \

Books, the author

Scikit-learn, Keras, and TensorFlow: A Practical Guide to Machine Learning (2nd Edition) was written by Aurelien Geron, a French AgroParisTech graduate and former Google Youtube video categorization project leader. Founded several companies and served as CTO and was also a lecturer at AgroParisTech. He is now a machine learning consultant.

Version changes

Overall, the second version has a lot of new content compared to the first version, most notably the use of SciKit-Learn and TensorFlow, and the second version has the Keras deep learning framework. \

In terms of content, the second edition adds more cutting-edge knowledge of machine learning, including: unsupervised learning, training deep networks, computer vision, natural language processing and so on.

Detailed version updates can be found here:

Github.com/ageron/hand…

Books introduce

Like the first edition, the book is divided into two parts. The first part is the fundamentals of machine learning and contains chapters 1 to 9: \

  • Chapter 1. The Machine Learning Landscape
  • Chapter 2. End-to-End Machine Learning Project
  • Chapter 3. Classification
  • Chapter 4. Training Models
  • Chapter 5. Support Vector Machines
  • Chapter 6. Decision Trees
  • Chapter 7. Ensemble Learning and Random Forests
  • Chapter 8. Dimensionality Reduction
  • Chapter 9. Unsupervised Learning Techniques

The first part is similar to the first edition of the book, with the addition of chapter 9 on unsupervised learning.

The second part of the book is about neural networks and deep learning, which includes chapters 10 to 19: \

  • Chapter 10. Introduction to Artificial Neural Networks with Keras
  • Chapter 11. Training Deep Neural Networks
  • Chapter 12. Custom Models and Training with TensorFlow
  • Chapter 13. Loading and Preprocessing Data with TensorFlow
  • Chapter 14. Deep Computer Vision Using Convolutional Neural Networks
  • Chapter 15. Processing Sequences Using RNNs and CNNs
  • Chapter 16. Natural Language Processing with RNNs and Attention
  • Chapter 17. Representation Learning and Generative Learning Using Autoencoders and GANs
  • Chapter 18. Reinforcement Learning
  • Chapter 19. Training and Deploying TensorFlow Models at Scale

This part of deep learning is the most updated by the author, which is quite different from the first version. \

Code with books

The author has open-source the detailed code for all chapters of the book and posted it on GitHub, where it has already earned 5.3K stars. The project address is: \

Github.com/ageron/hand…

I have to say, the author’s accompanying code is very high quality! Readers of the first edition will know that the code for each chapter is an.ipynb file that can be opened with a Jupyter Notebook. In addition to the code, the corresponding documentation is quite extensive.

Form a complete set of resources

Hands-on Machine Learning with SciKit-Learn, Keras, and TensorFlow, 2nd Edition is now available as an electronic PDF for easy reading. The way to obtain is very simple, please reply in the background of this public account: ML2 can!

Note: the menu of the official account includes an AI cheat sheet, which is very suitable for learning on the commute.

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