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TensorFlow Machine Learning Cookbook电子书

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作       者:Nick McClure

出  版  社:Packt Publishing

出版时间:2017-02-01

字       数:253.9万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production In Detail 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 will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.
目录展开

TensorFlow Machine Learning Cookbook

Table of Contents

TensorFlow Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

eBooks, discount offers, and more

Why Subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Piracy

Questions

1. Getting Started with TensorFlow

Introduction

How TensorFlow Works

Getting ready

How to do it…

How it works…

See also

Declaring Tensors

Getting ready

How to do it…

How it works…

There's more…

Using Placeholders and Variables

Getting ready

How to do it…

How it works…

There's more…

Working with Matrices

Getting ready

How to do it…

How it works…

Declaring Operations

Getting ready

How to do it…

How it works…

There's more…

Implementing Activation Functions

Getting ready

How to do it…

How it works…

There's more…

Working with Data Sources

Getting ready

How to do it…

How it works…

See also

Additional Resources

Getting ready

How to do it…

See also

2. The TensorFlow Way

Introduction

Operations in a Computational Graph

Getting ready

How to do it…

How it works…

Layering Nested Operations

Getting ready

How to do it…

How it works…

There's more…

Working with Multiple Layers

Getting ready

How to do it…

How it works…

Implementing Loss Functions

Getting ready

How to do it…

How it works…

There's more…

Implementing Back Propagation

Getting ready

How to do it…

How it works…

There's more…

See also

Working with Batch and Stochastic Training

Getting ready

How to do it…

How it works…

There's more…

Combining Everything Together

Getting ready

How to do it…

How it works…

There's more…

See also

Evaluating Models

Getting ready

How to do it…

How it works…

3. Linear Regression

Introduction

Using the Matrix Inverse Method

Getting ready

How to do it…

How it works…

Implementing a Decomposition Method

Getting ready

How to do it…

How it works…

Learning The TensorFlow Way of Linear Regression

Getting ready

How to do it…

How it works…

Understanding Loss Functions in Linear Regression

Getting ready

How to do it…

How it works…

There's more…

Implementing Deming regression

Getting ready

How to do it…

How it works…

Implementing Lasso and Ridge Regression

Getting ready

How to do it…

How it works…

There's' more…

Implementing Elastic Net Regression

Getting ready

How to do it…

How it works…

Implementing Logistic Regression

Getting ready

How to do it…

How it works…

4. Support Vector Machines

Introduction

Working with a Linear SVM

Getting ready

How to do it…

How it works…

Reduction to Linear Regression

Getting ready

How to do it…

How it works…

Working with Kernels in TensorFlow

Getting ready

How to do it…

How it works…

There's more…

Implementing a Non-Linear SVM

Getting ready

How to do it…

How it works…

Implementing a Multi-Class SVM

Getting ready

How to do it…

How it works…

5. Nearest Neighbor Methods

Introduction

Working with Nearest Neighbors

Getting ready

How to do it…

How it works…

There's more…

Working with Text-Based Distances

Getting ready

How to do it…

How it works…

There's more…

Computing with Mixed Distance Functions

Getting ready

How to do it…

How it works…

There's more…

Using an Address Matching Example

Getting ready

How to do it…

How it works…

Using Nearest Neighbors for Image Recognition

Getting ready

How to do it…

How it works…

There's more…

6. Neural Networks

Introduction

Implementing Operational Gates

Getting ready

How to do it…

How it works…

Working with Gates and Activation Functions

Getting ready

How to do it…

How it works…

There's more…

Implementing a One-Layer Neural Network

Getting ready

How to do it…

How it works…

There's more…

Implementing Different Layers

Getting ready

How to do it…

How it works…

Using a Multilayer Neural Network

Getting ready

How to do it…

How it works…

Improving the Predictions of Linear Models

Getting ready

How to do it

How it works…

Learning to Play Tic Tac Toe

Getting ready

How to do it…

How it works…

7. Natural Language Processing

Introduction

Working with bag of words

Getting ready

How to do it…

How it works…

There's more…

Implementing TF-IDF

Getting ready

How to do it…

How it works…

There's more…

Working with Skip-gram Embeddings

Getting ready

How to do it…

How it works…

There's more…

Working with CBOW Embeddings

Getting ready

How to do it…

How it works…

There's more…

Making Predictions with Word2vec

Getting ready

How to do it…

How it works…

There's more…

Using Doc2vec for Sentiment Analysis

Getting ready

How to do it…

How it works…

8. Convolutional Neural Networks

Introduction

Implementing a Simpler CNN

Getting ready

How to do it…

How it works…

There's more…

See also

Implementing an Advanced CNN

Getting ready

How to do it…

How it works…

See also

Retraining Existing CNNs models

Getting ready

How to do it…

How it works…

See also

Applying Stylenet/Neural-Style

Getting ready

How to do it…

How it works…

See also

Implementing DeepDream

Getting ready

How to do it…

There's more…

See also

9. Recurrent Neural Networks

Introduction

Implementing RNN for Spam Prediction

Getting ready

How to do it…

How it works…

There's more…

Implementing an LSTM Model

Getting ready

How to do it…

How it works…

There's more…

Stacking multiple LSTM Layers

Getting ready

How to do it…

How it works…

Creating Sequence-to-Sequence Models

Getting ready

How to do it…

How it works…

There's more…

Training a Siamese Similarity Measure

Getting ready

How to do it…

There's more…

10. Taking TensorFlow to Production

Introduction

Implementing unit tests

Getting ready

How it works…

Using Multiple Executors

Getting ready

How to do it…

How it works…

There's more…

Parallelizing TensorFlow

Getting ready

How to do it…

How it works…

Taking TensorFlow to Production

Getting ready

How to do it…

How it works…

Productionalizing TensorFlow – An Example

Getting ready

How to do it…

How it works…

11. More with TensorFlow

Introduction

Visualizing graphs in Tensorboard

Getting ready

How to do it…

There's more…

Working with a Genetic Algorithm

Getting ready

How to do it…

How it works…

There's more…

Clustering Using K-Means

Getting ready

How to do it…

There's more…

Solving a System of ODEs

Getting ready

How to do it…

How it works…

See also

Index

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