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Learning PySpark

You're reading from  Learning PySpark

Product type Book
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Tomasz Drabas Tomasz Drabas
Profile icon Tomasz Drabas
Denny Lee Denny Lee
Profile icon Denny Lee
View More author details

Table of Contents (20) Chapters

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Spark 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Chapter 8. TensorFrames

This chapter will provide a high-level primer on the burgeoning field of Deep Learning and the reasons why it is important. It will provide the fundamentals surrounding feature learning and neural networks required for deep learning. As well, this chapter will provide a quick start for TensorFrames for Apache Spark.

In this chapter, you will learn about:

  • What is Deep Learning?

  • A primer on feature learning

  • What is feature engineering?

  • What is TensorFlow?

  • Introducing TensorFrames

  • TensorFrames – quick start

As you can see in the preceding breakdown, we will be initially discussing deep learning – more specifically we will start with neural networks.

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