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TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure , Sujit Pal

2 (3)

Buy this Book

TensorFlow Machine Learning Cookbook

2 (3)

By: Nick McClure , Sujit Pal

Buy this Book

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Free Chapter

Getting Started with TensorFlow

Introduction

How TensorFlow works

Declaring variables and tensors

Using placeholders and variables

Working with matrices

Declaring operations

Implementing activation functions

Working with data sources

Additional resources

The TensorFlow Way

Introduction

Operations in a computational graph

Layering nested operations

Working with multiple layers

Implementing loss functions

Implementing backpropagation

Working with batch and stochastic training

Combining everything together

Evaluating models

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

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

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

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

Natural Language Processing

Introduction

Working with bag-of-words embeddings

Implementing TF-IDF

Working with Skip-Gram embeddings

Working with CBOW embeddings

Making predictions with word2vec

Using doc2vec for sentiment analysis

Convolutional Neural Networks

Introduction

Implementing a simple CNN

Implementing an advanced CNN

Retraining existing CNN models

Applying stylenet and the neural-style project

Implementing DeepDream

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

Taking TensorFlow to Production

Introduction

Implementing unit tests

Using multiple executors

Parallelizing TensorFlow

Taking TensorFlow to production

An example of productionalizing TensorFlow

Using TensorFlow Serving

More with TensorFlow

Introduction

Visualizing graphs in TensorBoard

Working with a genetic algorithm

Clustering using k-means

Solving a system of ordinary differential equations

Using a random forest

Using TensorFlow with Keras

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Support Vector Machines

This chapter will cover some important recipes regarding how to use, implement, and evaluate Support Vector Machines (SVM) in TensorFlow. The following areas will be covered:

Working with a linear SVM
Reduction to linear regression
Working with kernels in TensorFlow
Implementing a non-linear SVM
Implementing a multi-class SVM

Both the prior-covered logistic regression and most of the SVMs in this chapter are binary predictors. While logistic regression tries to find any separating line that maximizes the distance (probabilistically), SVMs also try to minimize the error while maximizing the margin between classes. In general, if the problem has a large number of features compared to training examples, try logistic regression or a linear SVM. If the number of training examples is larger, or the data is not linearly separable, a SVM with a Gaussian kernel...

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