Hands-on machine learning with Scikit-Learn, Keras and TensorFlow : concepts, tools, and techniques to build intelligent systems / Aurelien Geron

Contents

I. The Fundamentals of Machine Learning

1. The Machine Learning Landscape

2. End-to-End Machine Learning Project

4. Training Models

5. Support Vector Machines

6. Decision Trees

7. Ensemble Learning and Random Forests

8. Dimensionality Reduction

9. Unsupervised Learning Techniques

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

15. Processing Sequences Using RNNs and CNNs

16. Natural Language Processing with RNNs and Attention

17. Autoencoders, GANs, and Diffusion Models

18. Reinforcement Learning

19. Training and Deploying TensorFlow Models at Scale

A. Machine Learning Project Checklist

B. Autodiff

C. Special Data Structures

D. TensorFlow Graphs

Index