Deep Learning with Hadoop (eBook)

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.

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Build, implement and scale distributed deep learning models for large-scale datasetsAbout This BookGet to grips with the deep learning concepts and set up Hadoop to put them to useImplement and parallelize deep learning models on Hadoop's YARN frameworkA comprehensive tutorial to distributed deep learning with HadoopWho This Book Is ForIf 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 LearnExplore Deep Learning and various models associated with itUnderstand the challenges of implementing distributed deep learning with Hadoop and how to overcome itImplement Convolutional Neural Network (CNN) with deeplearning4jDelve 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.In DetailThis 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.Style and approachThis book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers' knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.