Book description
Through a recent series of 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 bestselling book uses concrete examples, minimal theory, and productionready Python frameworks (ScikitLearn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.
 Use Scikitlearn to track an example ML project end to end
 Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
 Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
 Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
 Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Publisher resources
Table of contents

Preface
 The Machine Learning Tsunami
 Machine Learning in Your Projects
 Objective and Approach
 Code Examples
 Prerequisites
 Roadmap
 Changes Between the First and the Second Edition
 Changes Between the Second and the Third Edition
 Other Resources
 Conventions Used in This Book
 O’Reilly Online Learning
 How to Contact Us
 Acknowledgments
 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
 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
 The Architecture of the Visual Cortex
 Convolutional Layers
 Pooling Layers
 Implementing Pooling Layers with Keras
 CNN Architectures
 Implementing a ResNet34 CNN Using Keras
 Using Pretrained Models from Keras
 Pretrained Models for Transfer Learning
 Classification and Localization
 Object Detection
 Object Tracking
 Semantic Segmentation
 Exercises
 15. Processing Sequences Using RNNs and CNNs
 16. Natural Language Processing with RNNs and Attention
 17. Autoencoders, GANs, and Diffusion Models

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
 Overview of Some Popular RL Algorithms
 Exercises
 19. Training and Deploying TensorFlow Models at Scale
 A. Machine Learning Project Checklist
 B. Autodiff
 C. Special Data Structures
 D. TensorFlow Graphs
 Index
 About the Author
Product information
 Title: HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow, 3rd Edition
 Author(s):
 Release date: October 2022
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781098125974
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