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 ContentsChapter 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 Servers
Chapter 13: Convolutional Neural Networks
Chapter 14: Recurrent Neural Networks
Chapter 15: Autoencoders
Chapter 16: Reinforcement Learning