TensorFlow Machine Learning Cookbook (pdf)

Description

Key Features

• 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

Book Description

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.

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

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.

Table of Contents

1. Getting Started with TensorFlow
2. The TensorFlow Way
3. Linear Regression
4. Support Vector Machines
5. Nearest Neighbor Methods
6. Neural Networks
7. Natural Language Processing
8. Convolutional Neural Networks
9. Recurrent Neural Networks
10. Taking TensorFlow to Production
11. More with TensorFlow