Book description
Graphics in this book are printed in black and white.
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 productionready Python frameworks—scikitlearn 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 scikitlearn to track an example machinelearning project endtoend
 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
 Preface
 I. The Fundamentals of Machine Learning
 1. The Machine Learning Landscape
 2. EndtoEnd Machine Learning Project
 3. Classification
 4. Training Models
 5. Support Vector Machines
 6. Decision Trees
 7. Ensemble Learning and Random Forests
 8. Dimensionality Reduction
 II. Neural Networks and Deep Learning

9. Up and Running with TensorFlow
 Installation
 Creating Your First Graph and Running It in a Session
 Managing Graphs
 Lifecycle of a Node Value
 Linear Regression with TensorFlow
 Implementing Gradient Descent
 Feeding Data to the Training Algorithm
 Saving and Restoring Models
 Visualizing the Graph and Training Curves Using TensorBoard
 Name Scopes
 Modularity
 Sharing Variables
 Exercises
 10. Introduction to Artificial Neural Networks
 11. Training Deep Neural Nets
 12. Distributing TensorFlow Across Devices and Servers
 13. Convolutional Neural Networks
 14. Recurrent Neural Networks
 15. Autoencoders

16. Reinforcement Learning
 Learning to Optimize Rewards
 Policy Search
 Introduction to OpenAI Gym
 Neural Network Policies
 Evaluating Actions: The Credit Assignment Problem
 Policy Gradients
 Markov Decision Processes
 Temporal Difference Learning and QLearning
 Learning to Play Ms. PacMan Using the DQN Algorithm
 Exercises
 Thank You!

A. Exercise Solutions
 Chapter 1: The Machine Learning Landscape
 Chapter 2: EndtoEnd 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: 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
 B. Machine Learning Project Checklist
 C. SVM Dual Problem
 D. Autodiff
 E. Other Popular ANN Architectures
 Index
Product information
 Title: HandsOn Machine Learning with ScikitLearn and TensorFlow
 Author(s):
 Release date: March 2017
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781491962299
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