TensorFlow Machine Learning Cookbook

Solving a System of ODEs

Clustering Using K-Means

Working with a Genetic Algorithm

There's more…

Visualizing graphs in Tensorboard

Introduction

Chapter 11. More with TensorFlow

Productionalizing TensorFlow – An Example

Taking TensorFlow to Production

Parallelizing TensorFlow

Using Multiple Executors

Implementing unit tests

Introduction

Chapter 10. Taking TensorFlow to Production

Training a Siamese Similarity Measure

Creating Sequence-to-Sequence Models

Stacking multiple LSTM Layers

Implementing an LSTM Model

Implementing RNN for Spam Prediction

Introduction

Chapter 9. Recurrent Neural Networks

Implementing DeepDream

Applying Stylenet/Neural-Style

Retraining Existing CNNs models

Implementing an Advanced CNN

Implementing a Simpler CNN

Introduction

Chapter 8. Convolutional Neural Networks

Using Doc2vec for Sentiment Analysis

Making Predictions with Word2vec

Working with CBOW Embeddings

Working with Skip-gram Embeddings

Implementing TF-IDF

Working with bag of words

Introduction

Chapter 7. Natural Language Processing

Learning to Play Tic Tac Toe

Improving the Predictions of Linear Models

Using a Multilayer Neural Network

Implementing Different Layers

Implementing a One-Layer Neural Network

Working with Gates and Activation Functions

Implementing Operational Gates

Introduction

Chapter 6. Neural Networks

Using Nearest Neighbors for Image Recognition

Using an Address Matching Example

Computing with Mixed Distance Functions

Working with Text-Based Distances

Working with Nearest Neighbors

Introduction

Chapter 5. Nearest Neighbor Methods

Implementing a Multi-Class SVM

Implementing a Non-Linear SVM

Working with Kernels in TensorFlow

Reduction to Linear Regression

Working with a Linear SVM

Introduction

Chapter 4. Support Vector Machines

Implementing Logistic Regression

Implementing Elastic Net Regression

Implementing Lasso and Ridge Regression

Implementing Deming regression

Understanding Loss Functions in Linear Regression

Learning The TensorFlow Way of Linear Regression

Implementing a Decomposition Method

Using the Matrix Inverse Method

Introduction

Chapter 3. Linear Regression

Evaluating Models

Combining Everything Together

Working with Batch and Stochastic Training

Implementing Back Propagation

Implementing Loss Functions

Working with Multiple Layers

Layering Nested Operations

Operations in a Computational Graph

Introduction

Chapter 2. The TensorFlow Way

Additional Resources

Working with Data Sources

Implementing Activation Functions

Declaring Operations

Working with Matrices

Using Placeholders and Variables

Declaring Tensors

How TensorFlow Works

Introduction

Chapter 1. Getting Started with TensorFlow

Customer support

Reader feedback

Conventions

Sections

Who this book is for

What you need for this book

What this book covers

Preface

Customer Feedback

eBooks discount offers and more

www.PacktPub.com

About the Reviewer

About the Author

Credits

TensorFlow Machine Learning Cookbook

coverpage

coverpage

TensorFlow Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

eBooks discount offers and more

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Chapter 1. Getting Started with TensorFlow

Introduction

How TensorFlow Works

Declaring Tensors

Using Placeholders and Variables

Working with Matrices

Declaring Operations

Implementing Activation Functions

Working with Data Sources

Additional Resources

Chapter 2. The TensorFlow Way

Introduction

Operations in a Computational Graph

Layering Nested Operations

Working with Multiple Layers

Implementing Loss Functions

Implementing Back Propagation

Working with Batch and Stochastic Training

Combining Everything Together

Evaluating Models

Chapter 3. Linear Regression

Introduction

Using the Matrix Inverse Method

Implementing a Decomposition Method

Learning The TensorFlow Way of Linear Regression

Understanding Loss Functions in Linear Regression

Implementing Deming regression

Implementing Lasso and Ridge Regression

Implementing Elastic Net Regression

Implementing Logistic Regression

Chapter 4. Support Vector Machines

Introduction

Working with a Linear SVM

Reduction to Linear Regression

Working with Kernels in TensorFlow

Implementing a Non-Linear SVM

Implementing a Multi-Class SVM

Chapter 5. Nearest Neighbor Methods

Introduction

Working with Nearest Neighbors

Working with Text-Based Distances

Computing with Mixed Distance Functions

Using an Address Matching Example

Using Nearest Neighbors for Image Recognition

Chapter 6. Neural Networks

Introduction

Implementing Operational Gates

Working with Gates and Activation Functions

Implementing a One-Layer Neural Network

Implementing Different Layers

Using a Multilayer Neural Network

Improving the Predictions of Linear Models

Learning to Play Tic Tac Toe

Chapter 7. Natural Language Processing

Introduction

Working with bag of words

Implementing TF-IDF

Working with Skip-gram Embeddings

Working with CBOW Embeddings

Making Predictions with Word2vec

Using Doc2vec for Sentiment Analysis

Chapter 8. Convolutional Neural Networks

Introduction

Implementing a Simpler CNN

Implementing an Advanced CNN

Retraining Existing CNNs models

Applying Stylenet/Neural-Style

Implementing DeepDream

Chapter 9. Recurrent Neural Networks

Introduction

Implementing RNN for Spam Prediction

Implementing an LSTM Model

Stacking multiple LSTM Layers

Creating Sequence-to-Sequence Models

Training a Siamese Similarity Measure

Chapter 10. Taking TensorFlow to Production

Introduction

Implementing unit tests

Using Multiple Executors

Parallelizing TensorFlow

Taking TensorFlow to Production

Productionalizing TensorFlow – An Example

Chapter 11. More with TensorFlow

Introduction

Visualizing graphs in Tensorboard

There's more…

Working with a Genetic Algorithm

Clustering Using K-Means

Solving a System of ODEs