Deep Learning with Hadoop.pdf

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Deep Learning with Hadoop

Credits

About the Author

About the Reviewers

www.PacktPub.com

Why subscribe?

Customer Feedback

Dedication

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Introduction to Deep Learning

Getting started with deep learning

Deep feed-forward networks

Various learning algorithms

Unsupervised learning

Supervised learning

Semi-supervised learning

Deep learning terminologies

Deep learning: A revolution in Artificial Intelligence

Motivations for deep learning

The curse of dimensionality

The vanishing gradient problem

Distributed representation

Classification of deep learning networks

Deep generative or unsupervised models

Deep discriminate models

Summary

2. Distributed Deep Learning for Large-Scale Data

Deep learning for massive amounts of data

Challenges of deep learning for big data

Challenges of deep learning due to massive volumes of data (first V)

Challenges of deep learning from a high variety of data (second V)

Challenges of deep learning from a high velocity of data (third V)

Challenges of deep learning to maintain the veracity of data (fourth V)

Distributed deep learning and Hadoop

Map-Reduce

Iterative Map-Reduce

Yet Another Resource Negotiator (YARN)

Important characteristics for distributed deep learning design

Deeplearning4j - an open source distributed framework for deep learning

Major features of Deeplearning4j

Summary of functionalities of Deeplearning4j

Setting up Deeplearning4j on Hadoop YARN

Getting familiar with Deeplearning4j

Integration of Hadoop YARN and Spark for distributed deep learning

Rules to configure memory allocation for Spark on Hadoop YARN

Summary

3. Convolutional Neural Network

Understanding convolution

Background of a CNN

Architecture overview

Basic layers of CNN

Importance of depth in a CNN

Convolutional layer

Sparse connectivity

Improved time complexity

Parameter sharing

Improved space complexity

Equivariant representations

Choosing the hyperparameters for Convolutional layers

Depth

Stride

Zero-padding

Mathematical formulation of hyperparameters

Effect of zero-padding

ReLU (Rectified Linear Units) layers

Advantages of ReLU over the sigmoid function

Pooling layer

Where is it useful, and where is it not?

Fully connected layer

Distributed deep CNN

Most popular aggressive deep neural networks and their configurations

Training time - major challenges associated with deep neural networks

Hadoop for deep CNNs

Convolutional layer using Deeplearning4j

Loading data

Model configuration

Training and evaluation

Summary

4. Recurrent Neural Network

What makes recurrent networks distinctive from others?

Recurrent neural networks(RNNs)

Unfolding recurrent computations

Advantages of a model unfolded in time

Memory of RNNs

Architecture

Backpropagation through time (BPTT)

Error computation

Long short-term memory

Problem with deep backpropagation with time

Long short-term memory

Bi-directional RNNs

Shortfalls of RNNs

Solutions to overcome

Distributed deep RNNs

RNNs with Deeplearning4j

Summary

5. Restricted Boltzmann Machines

Energy-based models

Boltzmann machines

How Boltzmann machines learn

Shortfall

Restricted Boltzmann machine

The basic architecture

How RBMs work

Convolutional Restricted Boltzmann machines

Stacked Convolutional Restricted Boltzmann machines

Deep Belief networks

Greedy layer-wise training

Distributed Deep Belief network

Distributed training of Restricted Boltzmann machines

Distributed training of Deep Belief networks

Distributed back propagation algorithm

Performance evaluation of RBMs and DBNs

Drastic improvement in training time

Implementation using Deeplearning4j

Restricted Boltzmann machines

Deep Belief networks

Summary

6. Autoencoders

Autoencoder

Regularized autoencoders

Sparse autoencoders

Sparse coding

Sparse autoencoders

The k-Sparse autoencoder

How to select the sparsity level k

Effect of sparsity level

Deep autoencoders

Training of deep autoencoders

Implementation of deep autoencoders using Deeplearning4j

Denoising autoencoder

Architecture of a Denoising autoencoder

Stacked denoising autoencoders

Implementation of a stacked denoising autoencoder using Deeplearning4j

Applications of autoencoders

Summary

7. Miscellaneous Deep Learning Operations using Hadoop

Distributed video decoding in Hadoop

Large-scale image processing using Hadoop

Application of Map-Reduce jobs

Natural language processing using Hadoop

Web crawler

Extraction of keyword and module for natural language processing

Estimation of relevant keywords from a page

Summary

1. References

Table of Contents Deep Learning with Hadoop Credits About the Author About the Reviewers www.PacktPub.com Why subscribe? Customer Feedback Dedication Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Introduction to Deep Learning Getting started with deep learning Deep feed-forward networks Various learning algorithms Unsupervised learning Supervised learning Semi-supervised learning Deep learning terminologies Deep learning: A revolution in Artificial Intelligence Motivations for deep learning The curse of dimensionality The vanishing gradient problem Distributed representation Classification of deep learning networks Deep generative or unsupervised models Deep discriminate models 2

Summary 2. Distributed Deep Learning for Large-Scale Data Deep learning for massive amounts of data Challenges of deep learning for big data Challenges of deep learning due to massive volumes of data Challenges of deep learning from a high variety of data Challenges of deep learning from a high velocity of data (third (first V) (second V) V) Challenges of deep learning to maintain the veracity of data (fourth V) Distributed deep learning and Hadoop Map-Reduce Iterative Map-Reduce Yet Another Resource Negotiator (YARN) Important characteristics for distributed deep learning design Deeplearning4j - an open source distributed framework for deep learning Major features of Deeplearning4j Summary of functionalities of Deeplearning4j Setting up Deeplearning4j on Hadoop YARN Getting familiar with Deeplearning4j Integration of Hadoop YARN and Spark for distributed deep Rules to configure memory allocation for Spark on Hadoop learning YARN Summary 3. Convolutional Neural Network Understanding convolution Background of a CNN Architecture overview Basic layers of CNN Importance of depth in a CNN Convolutional layer Sparse connectivity Improved time complexity Parameter sharing Improved space complexity Equivariant representations 3

Choosing the hyperparameters for Convolutional layers Depth Stride Zero-padding Mathematical formulation of hyperparameters Effect of zero-padding ReLU (Rectified Linear Units) layers Advantages of ReLU over the sigmoid function Pooling layer Where is it useful, and where is it not? Fully connected layer Distributed deep CNN configurations networks Most popular aggressive deep neural networks and their Training time - major challenges associated with deep neural Hadoop for deep CNNs Convolutional layer using Deeplearning4j Bi-directional RNNs Shortfalls of RNNs Solutions to overcome Distributed deep RNNs RNNs with Deeplearning4j Summary 4 Loading data Model configuration Training and evaluation Summary 4. Recurrent Neural Network What makes recurrent networks distinctive from others? Recurrent neural networks(RNNs) Unfolding recurrent computations Advantages of a model unfolded in time Memory of RNNs Architecture Backpropagation through time (BPTT) Error computation Long short-term memory Problem with deep backpropagation with time Long short-term memory

Convolutional Restricted Boltzmann machines Stacked Convolutional Restricted Boltzmann machines Deep Belief networks Greedy layer-wise training Distributed Deep Belief network Distributed training of Restricted Boltzmann machines Distributed training of Deep Belief networks Distributed back propagation algorithm Performance evaluation of RBMs and DBNs Drastic improvement in training time Implementation using Deeplearning4j Restricted Boltzmann machines Deep Belief networks Summary 6. Autoencoders Autoencoder Regularized autoencoders Sparse autoencoders Sparse coding Sparse autoencoders The k-Sparse autoencoder How to select the sparsity level k Effect of sparsity level Deep autoencoders 5. Restricted Boltzmann Machines Energy-based models Boltzmann machines How Boltzmann machines learn Shortfall Restricted Boltzmann machine The basic architecture How RBMs work Training of deep autoencoders Implementation of deep autoencoders using Deeplearning4j Denoising autoencoder Architecture of a Denoising autoencoder Stacked denoising autoencoders Implementation of a stacked denoising autoencoder using Deeplearning4j Applications of autoencoders 5

Summary 7. Miscellaneous Deep Learning Operations using Hadoop Distributed video decoding in Hadoop Large-scale image processing using Hadoop Application of Map-Reduce jobs Natural language processing using Hadoop Web crawler Extraction of keyword and module for natural language Estimation of relevant keywords from a page processing Summary 1. References 6

Deep Learning with Hadoop 7

Deep Learning with Hadoop Copyright © 2017 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: February 2017 Production reference: 1130217 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78712-476-9 www.packtpub.com 8

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