Book description
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
Publisher resources
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
 9. Unsupervised Learning Techniques
 II. Neural Networks and Deep Learning

10. Introduction to Artificial Neural Networks with Keras
 From Biological to Artificial Neurons

Implementing MLPs with Keras
 Installing TensorFlow 2
 Building an Image Classifier Using the Sequential API
 Building a Regression MLP Using the Sequential API
 Building Complex Models Using the Functional API
 Using the Subclassing API to Build Dynamic Models
 Saving and Restoring a Model
 Using Callbacks
 Using TensorBoard for Visualization
 FineTuning Neural Network Hyperparameters
 Exercises
 11. Training Deep Neural Networks
 12. Custom Models and Training with TensorFlow
 13. Loading and Preprocessing Data with TensorFlow
 14. Deep Computer Vision Using Convolutional Neural Networks
 15. Processing Sequences Using RNNs and CNNs
 16. Natural Language Processing with RNNs and Attention
 17. Representation Learning and Generative Learning Using Autoencoders and GANs

18. 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
 QLearning
 Implementing Deep QLearning
 Deep QLearning Variants

The TFAgents Library
 Installing TFAgents
 TFAgents Environments
 Environment Specifications
 Environment Wrappers and Atari Preprocessing
 Training Architecture
 Creating the Deep QNetwork
 Creating the DQN Agent
 Creating the Replay Buffer and the Corresponding Observer
 Creating Training Metrics
 Creating the Collect Driver
 Creating the Dataset
 Creating the Training Loop
 Overview of Some Popular RL Algorithms
 Exercises
 19. Training and Deploying TensorFlow Models at Scale

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: Unsupervised Learning Techniques
 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
 B. Machine Learning Project Checklist
 C. SVM Dual Problem
 D. Autodiff
 E. Other Popular ANN Architectures
 F. Special Data Structures
 G. TensorFlow Graphs
 Index
Product information
 Title: HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow, 2nd Edition
 Author(s):
 Release date: September 2019
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781492032649
You might also like
book
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow, 3rd Edition
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. …
book
Machine Learning with PyTorch and ScikitLearn
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide …
video
Deep Learning with TensorFlow, Keras, and PyTorch
7+ Hours of Video Instruction An intuitive, applicationfocused introduction to deep learning and TensorFlow, Keras, and …
book
Generative Deep Learning, 2nd Edition
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and …