Table of Contents
Chapter 1. A machine-learning odyssey
1.1. Machine-learning fundamentals
1.2. Data representation and features
Chapter 2. TensorFlow essentials
2.1. Ensuring that TensorFlow works
2.4. Executing operators with sessions
2.7. Saving and loading variables
2.8. Visualizing data using TensorBoard
Chapter 3. Linear regression and beyond
Chapter 4. A gentle introduction to classification
4.3. Using linear regression for classification
4.4. Using logistic regression
Chapter 5. Automatically clustering data
5.1. Traversing files in TensorFlow
5.2. Extracting features from audio
5.5. Clustering using a self-organizing map
Chapter 6. Hidden Markov models
6.1. Example of a not-so-interpretable model
6.6. Uses of hidden Markov models
3. The neural network paradigm
Chapter 7. A peek into autoencoders
Chapter 8. Reinforcement learning
8.2. Applying reinforcement learning
8.3. Implementing reinforcement learning
Chapter 9. Convolutional neural networks
9.1. Drawback of neural networks
9.2. Convolutional neural networks
9.4. Implementing a convolutional neural network in TensorFlow
9.5. Tips and tricks to improve performance
Chapter 10. Recurrent neural networks
10.2. Introduction to recurrent neural networks
10.3. Implementing a recurrent neural network
10.4. A predictive model for time-series data
Chapter 11. Sequence-to-sequence models for chatbots
11.1. Building on classification and RNNs
A.1. Installing TensorFlow by using Docker
A.1.1. Installing Docker on Windows
A.1.2. Installing Docker on Linux