Book description
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.
Table of contents
-
TensorFlow Machine Learning Cookbook
- Table of Contents
- TensorFlow Machine Learning Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- 1. Getting Started with TensorFlow
- 2. The TensorFlow Way
-
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
- 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
- Index
Product information
- Title: TensorFlow Machine Learning Cookbook
- Author(s):
- Release date: February 2017
- Publisher(s): Packt Publishing
- ISBN: 9781786462169
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