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Hands-On Deep Learning with Apache Spark

You're reading from   Hands-On Deep Learning with Apache Spark Build and deploy distributed deep learning applications on Apache Spark

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788994613
Length 322 pages
Edition 1st Edition
Languages
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Author (1):
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Guglielmo Iozzia Guglielmo Iozzia
Author Profile Icon Guglielmo Iozzia
Guglielmo Iozzia
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Table of Contents (19) Chapters Close

Preface 1. The Apache Spark Ecosystem FREE CHAPTER 2. Deep Learning Basics 3. Extract, Transform, Load 4. Streaming 5. Convolutional Neural Networks 6. Recurrent Neural Networks 7. Training Neural Networks with Spark 8. Monitoring and Debugging Neural Network Training 9. Interpreting Neural Network Output 10. Deploying on a Distributed System 11. NLP Basics 12. Textual Analysis and Deep Learning 13. Convolution 14. Image Classification 15. What's Next for Deep Learning? 16. Other Books You May Enjoy Appendix A: Functional Programming in Scala 1. Appendix B: Image Data Preparation for Spark

Deep Learning Basics

In this chapter, I am going to introduce the core concepts of Deep Learning (DL), the relationship it has with Machine Learning (ML) and Artificial Intelligence (AI), the different types of multilayered neural networks, and a list of real-world practical applications. I will try to skip mathematical equations as much as possible and keep the description very high level, with no reference to code examples. The goal of this chapter is to make readers aware of what DL really is and what you can do with it, while the following chapters will go much more into the details of this, with lots of practical code examples in Scala and Python (where this programming language can be used).

This chapter will cover the following topics:

  • DL concepts
  • Deep neural networks (DNNs)
  • Practical applications of DL
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