Deep Learning with Hadoop

Dipayan Dev

• Publisher: Packt Publishing
• Year: 2017
• ISBN:
  9781787121232
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

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

Key Features

• 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

Book Description

This book will teach you how to deploylarge-scale dataset in deep neural networks with Hadoop foroptimal performance.Starting with understanding what deeplearning is, and what the various modelsassociated with deep neural networks are, thisbook will then show you how to set up theHadoop environment for deep learning.In this book, you will also learn how toovercome the challenges that you facewhile implementing distributed deeplearning with large-scale unstructured datasets. The book willalso show you how you can implementand 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 explanationsand visual representations to helpyou understand the design and implementationsof Recurrent Neural network and Denoising Autoencoders withDeeplearning4j. To give you a morepractical perspective, the book will alsoteach you the implementation of large-scale video processing, image processing andnatural language processing on Hadoop.By the end of this book, you willknow how to deploy various deep neural networks indistributed systems using Hadoop.

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.

Who this book is 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.

]]>

Table of contents

• Deep Learning with Hadoop