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Deep Learning with Hadoop

You're reading from   Deep Learning with Hadoop Distributed Deep Learning with Large-Scale Data

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781787124769
Length 206 pages
Edition 1st Edition
Languages
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Author (1):
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Dipayan Dev Dipayan Dev
Author Profile Icon Dipayan Dev
Dipayan Dev
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Toc

Table of Contents (9) Chapters Close

Preface 1. Introduction to Deep Learning FREE CHAPTER 2. Distributed Deep Learning for Large-Scale Data 3. Convolutional Neural Network 4. Recurrent Neural Network 5. Restricted Boltzmann Machines 6. Autoencoders 7. Miscellaneous Deep Learning Operations using Hadoop 1. References

Summary

Over the past decade, we have had the privilege of hearing about the greatest inventions of deep learning from many of the great scientists and companies working in Artificial Intelligence. Deep learning is an approach to machine learning which has shown tremendous growth in its usefulness and popularity in the last few years. The reason is mostly due to its capability to work with large datasets involving high dimensional data, resolving major issues such as vanishing gradient problems, and so on, and techniques to train deeper networks. In this chapter, we have explained most of these concepts in detail, and have also classified the various algorithms of deep learning, which will be elucidated in detail in subsequent chapters.

The next chapter of this book will introduce the association of big data with deep learning. The chapter will mainly focus on how deep learning plays a major role in extracting valuable information from large-scale data.

You have been reading a chapter from
Deep Learning with Hadoop
Published in: Feb 2017
Publisher: Packt
ISBN-13: 9781787124769
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