Preface

PartⅠ. 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

Part Ⅱ. Neural Networks and Deep Learning
10.Introduction to Artificial Neural Network with Keras
11.Training Deep Neural Networks
12.Custom Models and Traning with TensoFlow
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
19.Traning and Deploying TensorFlow Models at Scale

A. Exercise Solutions
B. Machine Learning Project Checklist
C. SVM Dual Problem
D. Autodiff
E. Other Popular ANN Architectures
F. Special Data Structures
G.TensorFlow Graphs

Index