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 production-ready Python frameworks (Scikit-Learn, 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 Scikit-learn 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. End-to-End 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 ResNet-34 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
- Q-Learning
- Implementing Deep Q-Learning
- Deep Q-Learning 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: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
- Author(s):
- Release date: October 2022
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098125974
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