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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

Introducing DL

DL is a subset of ML that can solve particularly hard and large-scale problems in areas such as Natural Language Processing (NLP) and image classification. The expression DL is sometimes used in an interchangeable way with ML and AI, but both ML and DL are subsets of AI. AI is the broader concept that is implemented through ML. DL is a way of implementing ML, and involves neural network-based algorithms:

Figure 2.1

AI is considered the ability of a machine (it could be any computer-controlled device or robot) to perform tasks that are typically associated with humans. It was introduced in the 1950s, with the goal of reducing human interaction, thereby making the machine do all the work. This concept is mainly applied to the development of systems that typically require human intellectual processes and/or the ability to learn from past experiences.

ML is an approach...

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