Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent System 英文高清版pdf 随书代码下载地址： https://github.com/ageron/handson-ml

著名的《Hands-On Machine Learning with Scikit-Learn and TensorFlow》的中文翻译版PDF，由ApacheCN中文社区的机器学习爱好者们共同翻译。一个人可以走的很快，但是一群人却可以走的更远。 这本书可以带领你入门机器学习，并掌握常用机器学习库的编程实现，在ML路上走得更远。

Hands-On Transfer Learning with Python（带书签PDF+代码）,迁移学习Python实战，by Dipanjan Sarkar。

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details Table of Contents Chapter 1: The Machine Learning Landscape Chapter 2: End-to-End Machine Learning Project Chapter 3: Classification Chapter 4: Training Linear Models Chapter 5: Support Vector Machines Chapter 6: Decision Trees Chapter 7: Ensemble Learning and Random Forests Chapter 8: Dimensionality Reduction Chapter 9: Up and Running with TensorFlow Chapter 10: Introduction to Artificial Neural Networks Chapter 11: Training Deep Neural Nets Chapter 12: Distributing TensorFlow Across Devices and S