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Deep Learning with Hadoop
Deep Learning with Hadoop

Deep Learning with Hadoop: Distributed Deep Learning with Large-Scale Data

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Deep Learning with Hadoop

Chapter 2.  Distributed Deep Learning for Large-Scale Data

 

"In God we trust, all others must bring data"

 
 --W. Edwards Deming

In this exponentially growing digital world, big data and deep learning are the two hottest technical trends. Deep learning and big data are two interrelated topics in the world of data science, and in terms of technological growth, both are critically interconnected and equally significant.

Digital data and cloud storage follow a generic law, termed as Moore's law [50], which roughly states that the world's data are doubling every two years; however, the cost of storing that data decreases at approximately the same rate. This profusion of data generates more features and verities, hence, to extract all the valuable information out of it, better deep learning models should be built.

This voluminous availability of data helps to bring huge opportunities for multiple sectors. Moreover, big data, with its analytic part...

Deep learning for massive amounts of data

In this Exa-Byte scale era, the data are increasing at an exponential rate. This growth of data are analyzed by many organizations and researchers in various ways, and also for so many different purposes. According to the survey of International Data Corporation (IDC), the Internet is processing approximately 2 Petabytes of data every day [51]. In 2006, the size of digital data was around 0.18 ZB, whereas this volume has increased to 1.8 ZB in 2011. Up to 2015, it was expected to reach up to 10 ZB in size, and by 2020, its volume in the world will reach up to approximately 30 ZB to 35 ZB. The timeline of this data mountain is shown in Figure 2.1. These immense amounts of data in the digital world are formally termed as big data.

 

"The world of Big Data is on fire"

 
 --The Economist, Sept 2011

Deep learning for massive amounts of data

Figure 2.1: Figure shows the increasing trend of data for a time span of around 20 years

Facebook has almost 21 PB in 200M objects...

Challenges of deep learning for big data

The potential of big data is certainly noteworthy. However, to fully extract valuable information at this scale, we would require new innovations and promising algorithms to address many of these related technical problems. For example, to train the models, most of the traditional machine learning algorithms load the data in memory. But with a massive amount of data, this approach will surely not be feasible, as the system might run out of memory. To overcome all these gritty problems, and get the most out of the big data with the deep learning techniques, we will require brain storming.

Although, as discussed in the earlier section, large-scale deep learning has achieved many accomplishments in the past decade, this field is still in a growing phase. Big data is constantly raising limitations with its 4Vs. Therefore, to tackle all of those, many more advancements in the models need to take place.

Challenges of deep learning due to massive...

Distributed deep learning and Hadoop

From the earlier sections of this chapter, we already have enough insights on why and how the relationship of deep learning and big data can bring major changes to the research community. Also, a centralized system is not going to help this relationship substantially with the course of time. Hence, distribution of the deep learning network across multiple servers has become the primary goal of the current deep learning practitioners. However, dealing with big data in a distributed environment is always associated with several challenges. Most of those are explained in-depth in the previous section. These include dealing with higher dimensional data, data with too many features, amount of memory available to store, processing the massive Big datasets, and so on. Moreover, Big datasets have a high computational resource demand on CPU and memory time. So, the reduction of processing time has become an extremely significant criterion. The following are the...

Deeplearning4j - an open source distributed framework for deep learning

Deeplearning4j (DL4J) [82] is an open source deep learning framework which is written for JVM, and mainly used for commercial grade. The framework is written entirely in Java, and thus, the name '4j' is included. Because of its use with Java, Deeplearning4j has started to earn popularity with a much wider audience and range of practitioners.

This framework is basically composed of a distributed deep learning library that is integrated with Hadoop and Spark. With the help of Hadoop and Spark, we can very easily distribute the model and Big datasets, and run multiple GPUs and CPUs to perform parallel operations. Deeplearning4j has primarily shown substantial success in performing pattern recognition in images, sound, text, time series data, and so on. Apart from that, it can also be applied for various customer use cases such as facial recognition, fraud detection, business analytics, recommendation engines...

Deep learning for massive amounts of data


In this Exa-Byte scale era, the data are increasing at an exponential rate. This growth of data are analyzed by many organizations and researchers in various ways, and also for so many different purposes. According to the survey of International Data Corporation (IDC), the Internet is processing approximately 2 Petabytes of data every day [51]. In 2006, the size of digital data was around 0.18 ZB, whereas this volume has increased to 1.8 ZB in 2011. Up to 2015, it was expected to reach up to 10 ZB in size, and by 2020, its volume in the world will reach up to approximately 30 ZB to 35 ZB. The timeline of this data mountain is shown in Figure 2.1. These immense amounts of data in the digital world are formally termed as big data.

 

"The world of Big Data is on fire"

 
 --The Economist, Sept 2011

Figure 2.1: Figure shows the increasing trend of data for a time span of around 20 years

Facebook has almost 21 PB in 200M objects [52], whereas Jaguar ORNL...

Challenges of deep learning for big data


The potential of big data is certainly noteworthy. However, to fully extract valuable information at this scale, we would require new innovations and promising algorithms to address many of these related technical problems. For example, to train the models, most of the traditional machine learning algorithms load the data in memory. But with a massive amount of data, this approach will surely not be feasible, as the system might run out of memory. To overcome all these gritty problems, and get the most out of the big data with the deep learning techniques, we will require brain storming.

Although, as discussed in the earlier section, large-scale deep learning has achieved many accomplishments in the past decade, this field is still in a growing phase. Big data is constantly raising limitations with its 4Vs. Therefore, to tackle all of those, many more advancements in the models need to take place.

Challenges of deep learning due to massive volumes of...

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Key benefits

  • Get to grips with the deep learning concepts and set up Hadoop to put them to use
  • Implement and parallelize deep learning models on Hadoop’s YARN framework
  • A comprehensive tutorial to distributed deep learning with Hadoop

Description

This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann machines and autoencoder using the popular deep learning library Deeplearning4j. Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising Autoencoders with Deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop.

Who is this book for?

If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book.

What you will learn

  • Explore Deep Learning and various models associated with it
  • Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it
  • Implement Convolutional Neural Network (CNN) with Deeplearning4j
  • Delve into the implementation of Restricted Boltzmann machines (RBMs)
  • Understand the mathematical explanation for implementing Recurrent Neural Networks (RNNs)
  • Understand the design and implementation of Deep Belief Networks (DBN) and Deep Autoencoders using Deeplearning4j
  • Get hands on practice of deep learning and their implementation with Hadoop.

Product Details

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Publication date : Feb 20, 2017
Length: 206 pages
Edition : 1st
Language : English
ISBN-13 : 9781787124769
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Product Details

Publication date : Feb 20, 2017
Length: 206 pages
Edition : 1st
Language : English
ISBN-13 : 9781787124769
Vendor :
Apache
Category :
Languages :
Concepts :

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Table of Contents

8 Chapters
1. Introduction to Deep Learning Chevron down icon Chevron up icon
2. Distributed Deep Learning for Large-Scale Data Chevron down icon Chevron up icon
3. Convolutional Neural Network Chevron down icon Chevron up icon
4. Recurrent Neural Network Chevron down icon Chevron up icon
5. Restricted Boltzmann Machines Chevron down icon Chevron up icon
6. Autoencoders Chevron down icon Chevron up icon
7. Miscellaneous Deep Learning Operations using Hadoop Chevron down icon Chevron up icon
1. References Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(5 Ratings)
5 star 80%
4 star 20%
3 star 0%
2 star 0%
1 star 0%
Oleg Okun Nov 05, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you are looking for a book to learn deeplearning4j - A Java based Distributed Deep Learning framework - this is the book to read. It contains a lot of useful code to immediately start working with, which implements the main Deep Learning models in deeplearning4j: Convolutional Neural Networks, Recurrent Neural Networks, Restricted Bolzman Machines, and Autoencoders. For beginners to Deep Learning, the author explains a network architecture for each model, its strong and weak points, details of fine-tuning to pay attention to. The last chapter sketches the design of real-world applications of the models described in the previous chapters, such as distributed video decoding and intelligent web browsing.
Amazon Verified review Amazon
JAYASMITA DEB Mar 14, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A great book to know about distributed deep learning and is explained in a appropriate manner.
Amazon Verified review Amazon
Amazon Customer Mar 14, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was looking for a good use case of Xanadu, a big data platform technology that I'm now commercializing, in Machine Learning.Distributed deep learning exploiting large-scale datasets that is explained in this book in detail will be one of best use cases of Xanadu,which can show Xanadu's excellent functionality in deep learning applications. This book is an excellent reference to anyone who wants toexplore the distributed deep learning for big data applications.
Amazon Verified review Amazon
shreya dey Jul 23, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Liked the book. Must read... Useful. Information overloaded!
Amazon Verified review Amazon
Amazon Customer Jan 30, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Simple and easy to understand....With useful information
Amazon Verified review Amazon
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