TensorFlow Machine Learning Cookbook
eBook - ePub

TensorFlow Machine Learning Cookbook

Nick McClure

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  1. 370 pages
  2. English
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eBook - ePub

TensorFlow Machine Learning Cookbook

Nick McClure

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À propos de ce livre

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.

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Informations

Éditeur
Packt Publishing
Année
2017
ISBN
9781786462169
Édition
1
Sujet
Computer Science
Sous-sujet
Computer Vision & Pattern Recognition

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

TensorFlow Machine Learning Cookbook

Copyright © 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or revie...

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