A great guide to machine learning. It helped launch my third career!

*Machine Learning with TensorFlow* gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.

# Part 1: My Machine Learning Rig

## 1. A machine-learning odyssey

### 1.1. Machine learning fundamentals

#### 1.1.1. Parameters

#### 1.1.2. Learning and inference

### 1.2. Data representation and features

### 1.3. Distance Metrics

### 1.4. Types of Learning

#### 1.4.1. Supervised Learning

#### 1.4.2. Unsupervised Learning

#### 1.4.3. Reinforcement Learning

### 1.5. TensorFlow

### 1.6. Overview of future chapters

### 1.7. Summary

## 2. TensorFlow essentials

### 2.1. Ensuring TensorFlow works

### 2.2. Representing tensors

### 2.3. Creating operators

### 2.4. Executing operators with sessions

#### 2.4.1. Understanding code as a graph

#### 2.4.2. Session configurations

### 2.5. Writing code in Jupyter

### 2.6. Using variables

### 2.7. Saving and Loading Variables

### 2.8. Visualizing data using TensorBoard

#### 2.8.1. Implementing a moving average

#### 2.8.2. Visualizing the moving average

### 2.9. Summary

# Part 2: Core Learning Algorithms

## 3. Linear regression and beyond

### 3.1. Formal notation

#### 3.1.1. How do you know the regression algorithm is working?

### 3.2. Linear Regression

### 3.3. Polynomial Model

### 3.4. Regularization

### 3.5. Application of linear regression

### 3.6. Summary

## 4. A gentle introduction to classification

### 4.1. Formal Notation

### 4.2. Measuring Performance

#### 4.2.1. Accuracy

#### 4.2.2. Precision and Recall

#### 4.2.3. Receiver operating characteristic curve

### 4.3. Using linear regression for classification

### 4.4. Using logistic regression

#### 4.4.1. Solving one-dimensional logistic regression

#### 4.4.2. Solving two-dimensional logistic regression

### 4.5. Multiclass classifier

#### 4.5.1. One versus all

#### 4.5.2. One versus one

#### 4.5.3. Softmax regression

### 4.6. Application of classification

### 4.7. Summary

## 5. Automatically clustering data

### 5.1. Traversing files in TensorFlow

### 5.2. Extracting features from audio

### 5.3. K-means clustering

### 5.4. Audio segmentation

### 5.5. Clustering using a self-organizing map

### 5.6. Application of clustering

### 5.7. Summary

## 6. Hidden Markov models

### 6.1. Example of a not-so-interpretable model

### 6.2. Markov Model

### 6.3. Hidden Markov Model

### 6.4. Forward algorithm

### 6.5. Viterbi decode

### 6.6. Uses of Hidden Markov Models

#### 6.6.1. Modeling a video

#### 6.6.2. Modeling DNA

#### 6.6.3. Modeling an image

### 6.7. Application of hidden Markov models

### 6.8. Summary

# Part 3: The Neural Network Paradigm

## 7. A peek into autoencoders

### 7.1. Neural Networks

### 7.2. Autoencoder

### 7.3. Batch training

### 7.4. Working with images

### 7.5. Application of autoencoders

### 7.6. Summary

## 8. Reinforcement learning

### 8.1. Formal notions

#### 8.1.1. Policy

#### 8.1.2. Utility

### 8.2. Applying reinforcement learning

### 8.3. Implementation

### 8.4. Applications of reinforcement learning

### 8.5. Summary

## 9. Convolutional neural networks

### 9.1. Drawback of neural networks

### 9.2. Convolutional neural networks

### 9.3. Preparing the image

#### 9.3.1. Generate filters

#### 9.3.2. Convolve using filters

#### 9.3.3. Max-pooling

### 9.4. Implementing a convolutional neural network in TensorFlow

#### 9.4.1. Measuring performance

#### 9.4.2. Training the classifier

### 9.5. Tips and tricks to improve performance

### 9.6. Application of convolutional neural networks

### 9.7. Summary

## 10. Recurrent neural networks

### 10.1. Contextual information

### 10.2. Introduction to recurrent neural networks

### 10.3. Implementing a recurrent neural network

### 10.4. A predictive model for timeseries data

### 10.5. Application of recurrent neural networks

### 10.6. Summary

## 11. Sequence-to-sequence models for chatbots

### 11.1. Building on Classification and RNNs

### 11.2. Seq-to-seq architecture

### 11.3. Vector representation of symbols

### 11.4. Putting it all together

### 11.5. Gathering dialogue data

### 11.6. Summary

## 12. Utility landscape

### 12.1. Preference model

### 12.2. Image embedding

### 12.3. Ranking images

### 12.4. Summary

### 12.5. What’s next?

# Appendixes

## Appendix A: Installation

### A.1. Installing TensorFlow using Docker

#### A.1.1. Install Docker on Windows

#### A.1.2. Install Docker on Linux

#### A.1.3. Install Docker on OSX

#### A.1.4. How to user Docker

### A.2. Installing Matplotlib

## About the Technology

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.

## About the book

*Machine Learning with TensorFlow* gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.

## What's inside

- Matching your tasks to the right machine-learning and deep-learning approaches
- Visualizing algorithms with TensorBoard
- Understanding and using neural networks