Deep Learning with Hadoop 1st Edition by Dipayan Dev ISBN 9781787121232 1787121232

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ISBN 10: 1787121232
ISBN 13: 9781787121232
Author: Dipayan Dev

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.

Deep Learning with Hadoop 1st Edition Table of contents:

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

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Tags: Dipayan Dev, Deep Learning, Hadoop