Deep Learning with Hadoop (Paperback)

Dipayan Dev

Deep Learning with Hadoop (Paperback)
  • 出版商: Packt Publishing
  • 出版日期: 2017-02-17
  • 定價: $1,330
  • 售價: 6.0 折 $798
  • 語言: 英文
  • 頁數: 206
  • 裝訂: Paperback
  • ISBN: 1787124762
  • ISBN-13: 9781787124769
  • 相關分類: Hadoop、DeepLearning
  • 相關翻譯: Hadoop深度學習 (簡中版)

    • 立即出貨 (庫存 < 3)

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商品描述

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 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.

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.

About the Author

Dipayan Dev has completed his M.Tech from National Institute of Technology, Silchar with a first class first and is currently working as a software professional in Bengaluru, India. He has extensive knowledge and experience in non-relational database technologies, having primarily worked with large-scale data over the last few years. His core expertise lies in Hadoop Framework. During his postgraduation, Dipayan had built an infinite scalable framework for Hadoop, called Dr. Hadoop, which got published in top-tier SCI-E indexed journal of Springer. Dr. Hadoop has recently been cited by Goo Wikipedia in their Apache Hadoop article. Apart from that, he registers interest in a wide range of distributed system technologies, such as Redis, Apache Spark, Elasticsearch, Hive, Pig, Riak, and other NoSQL databases. Dipayan has also authored various research papers and book chapters, which are published by IEEE and top-tier Springer Journals.

Table of Contents

  1. Introduction to Deep Learning
  2. Distributed Deep Learning for Large-Scale Data
  3. Convolutional Neural Network
  4. Recurrent Neural Network
  5. Restricted Boltzmann Machines
  6. Autoencoders
  7. Miscellaneous Deep Learning Operations using Hadoop
  8. References

商品描述(中文翻譯)

主要特點


  • 深入了解深度學習概念並設置Hadoop以應用它們

  • 在Hadoop的YARN框架上實現並且並行化深度學習模型

  • 一個關於使用Hadoop進行分佈式深度學習的全面教程

書籍描述

本書將教你如何使用Hadoop在深度神經網絡中部署大規模數據集以獲得最佳性能。

從理解深度學習是什麼，以及與深度神經網絡相關的各種模型開始，本書將向你展示如何為深度學習設置Hadoop環境。在本書中，你還將學習如何克服在實現分佈式深度學習時面臨的挑戰，特別是處理大規模非結構化數據集。本書還將向你展示如何使用流行的深度學習庫deeplearning4j實現並且並行化廣泛使用的深度學習模型，如深度信念網絡、卷積神經網絡、循環神經網絡、受限玻爾茨曼機和自編碼器。

通過深入的數學解釋和視覺表示，幫助你理解使用deeplearning4j設計和實現循環神經網絡和去噪自編碼器。為了給你更實用的觀點，本書還將教你如何在Hadoop上實現大規模視頻處理、圖像處理和自然語言處理。

通過閱讀本書，你將學會如何在Hadoop上部署各種深度神經網絡。

你將學到什麼


  • 探索深度學習及其相關模型

  • 了解使用Hadoop實現分佈式深度學習的挑戰以及如何克服

  • 使用deeplearning4j實現卷積神經網絡（CNN）

  • 深入了解實現受限玻爾茨曼機（RBM）的數學解釋

  • 理解實現循環神經網絡（RNN）的數學解釋

  • 實踐深度學習並在Hadoop上實現

關於作者

Dipayan Dev畢業於印度國家技術學院Silchar分校，獲得一等一級碩士學位，目前在印度班加羅爾擔任軟體專業人員。他在非關聯式數據庫技術方面擁有豐富的知識和經驗，近年來主要從事大規模數據的工作。他的核心專長在於Hadoop框架。在研究生期間，Dipayan建立了一個名為Dr. Hadoop的無限可擴展Hadoop框架，該框架在Springer的頂級SCI-E索引期刊上發表。Dr. Hadoop最近被Goo Wikipedia引用在他們的Apache Hadoop文章中。除此之外，他還對Redis、Apache Spark、Elasticsearch、Hive、Pig、Riak和其他NoSQL數據庫等各種分佈式系統技術感興趣。Dipayan還撰寫了多篇研究論文和書籍章節，這些論文和章節由IEEE和頂級Springer期刊發表。

目錄


  1. 深度學習簡介

  2. 大規模數據的分佈式深度學習

  3. 卷積神經網絡

  4. 循環神經網絡

  5. 受限玻爾茨曼機

  6. 自編碼器

  7. 使用Hadoop進行其他深度學習操作

  8. 參考資料

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