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
Table of Contents
Part I. The Fundamentals of Machine Learning
Chapter 1. The Machine Learning Landscape
Chapter 2. End-to-End 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
Part II. Neural Networks and Deep Learning
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
Appendix A. Exercise Solutions
Appendix B. Machine Learning Project Checklist
Appendix C. SVM Dual Problem
Appendix D. Autodiff
Appendix E. Other Popular ANN Architectures
Appendix F. Special Data Structures
Appendix G. TensorFlow Graphs
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