Deep Learning with Hadoop

Build, implement and scale distributed deep learning models for large-scale datasets
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Deep Learning with Hadoop

Dipayan Dev

Build, implement and scale distributed deep learning models for large-scale datasets

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Book Details

ISBN 139781787124769
Paperback206 pages

Book 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.

Table of Contents

Chapter 1: Introduction to Deep Learning
Getting started with deep learning
Deep learning terminologies
Deep learning: A revolution in Artificial Intelligence
Classification of deep learning networks
Summary
Chapter 2: Distributed Deep Learning for Large-Scale Data
Deep learning for massive amounts of data
Challenges of deep learning for big data
Distributed deep learning and Hadoop
Deeplearning4j - an open source distributed framework for deep learning
Setting up Deeplearning4j on Hadoop YARN
Summary
Chapter 3: Convolutional Neural Network
Understanding convolution
Background of a CNN
Basic layers of CNN
Distributed deep CNN
Convolutional layer using Deeplearning4j
Summary
Chapter 4: Recurrent Neural Network
What makes recurrent networks distinctive from others?
Recurrent neural networks(RNNs)
Backpropagation through time (BPTT)
Long short-term memory
Bi-directional RNNs
Distributed deep RNNs
RNNs with Deeplearning4j
Summary
Chapter 5: Restricted Boltzmann Machines
Energy-based models
Boltzmann machines
Restricted Boltzmann machine
Convolutional Restricted Boltzmann machines
Deep Belief networks
Distributed Deep Belief network
Implementation using Deeplearning4j
Summary
Chapter 6: Autoencoders
Autoencoder
Sparse autoencoders
Deep autoencoders
Denoising autoencoder
Applications of autoencoders
Summary
Chapter 7: Miscellaneous Deep Learning Operations using Hadoop
Distributed video decoding in Hadoop
Large-scale image processing using Hadoop
Natural language processing using Hadoop
Summary

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 (RBM)
  • Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)
  • Get hands on practice of deep learning and their implementation with Hadoop.

Authors

Table of Contents

Chapter 1: Introduction to Deep Learning
Getting started with deep learning
Deep learning terminologies
Deep learning: A revolution in Artificial Intelligence
Classification of deep learning networks
Summary
Chapter 2: Distributed Deep Learning for Large-Scale Data
Deep learning for massive amounts of data
Challenges of deep learning for big data
Distributed deep learning and Hadoop
Deeplearning4j - an open source distributed framework for deep learning
Setting up Deeplearning4j on Hadoop YARN
Summary
Chapter 3: Convolutional Neural Network
Understanding convolution
Background of a CNN
Basic layers of CNN
Distributed deep CNN
Convolutional layer using Deeplearning4j
Summary
Chapter 4: Recurrent Neural Network
What makes recurrent networks distinctive from others?
Recurrent neural networks(RNNs)
Backpropagation through time (BPTT)
Long short-term memory
Bi-directional RNNs
Distributed deep RNNs
RNNs with Deeplearning4j
Summary
Chapter 5: Restricted Boltzmann Machines
Energy-based models
Boltzmann machines
Restricted Boltzmann machine
Convolutional Restricted Boltzmann machines
Deep Belief networks
Distributed Deep Belief network
Implementation using Deeplearning4j
Summary
Chapter 6: Autoencoders
Autoencoder
Sparse autoencoders
Deep autoencoders
Denoising autoencoder
Applications of autoencoders
Summary
Chapter 7: Miscellaneous Deep Learning Operations using Hadoop
Distributed video decoding in Hadoop
Large-scale image processing using Hadoop
Natural language processing using Hadoop
Summary

Book Details

ISBN 139781787124769
Paperback206 pages
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