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 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.
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
- Introduction to Deep Learning
- Distributed Deep Learning for Large-Scale Data
- Convolutional Neural Network
- Recurrent Neural Network
- Restricted Boltzmann Machines
- Deep Belief Networks
- Miscellaneous Deep Learning Operations on Hadoop
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