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  1. Deep Learning with Hadoop
    Autor*in: Dev, Dipayan
    Erschienen: 2017
    Verlag:  Packt Publishing, Birmingham ; ProQuest, Ann Arbor, Michigan

    Standort:
    Hessisches BibliotheksInformationsSystem HeBIS
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Hessisches BibliotheksInformationsSystem (HeBIS)
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781787121232
    Auflage/Ausgabe: 1st ed.
    Umfang: 1 Online-Ressource (200 pages)
    Bemerkung(en):

    Description based on publisher supplied metadata and other sources
  2. Deep learning with Hadoop
    build, implement and scale distributed deep learning models for large-scale datasets
    Autor*in: Dev, Dipayan
    Erschienen: 2017
    Verlag:  Packt, Birmingham, [England]

    Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Deep Learning -- Getting started with deep learning -- Deep feed-forward... mehr

    Standort:
    Technische Universität Chemnitz, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Südwestdeutscher Bibliotheksverbund (SWB)

    Standort:
    Leibniz-Fachhochschule Hannover, Bibliothek
    Fernleihe:
    keine Fernleihe
    Standort:
    Leibniz-Fachhochschule Hannover, Bibliothek
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Gemeinsamer Bibliotheksverbund (GBV)

    Standort:
    Hochschulbibliothek Friedensau
    Signatur:
    Online-Ressource
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Gemeinsamer Bibliotheksverbund (GBV)

    Standort:
    Universität Ulm, Kommunikations- und Informationszentrum, Bibliotheksservices
    Fernleihe:
    keine Fernleihe
    Link zum Verbundkatalog:
    Südwestdeutscher Bibliotheksverbund (SWB)

     

    Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Deep Learning -- Getting started with deep learning -- Deep feed-forward networks -- Various learning algorithms -- Unsupervised learning -- Supervised learning -- Semi-supervised learning -- Deep learning terminologies -- Deep learning: A revolution in Artificial Intelligence -- Motivations for deep learning -- The curse of dimensionality -- The vanishing gradient problem -- Distributed representation -- Classification of deep learning networks -- Deep generative or unsupervised models -- Deep discriminate models -- Summary -- Chapter 2: Distributed Deep Learning for Large-Scale Data -- Deep learning for massive amounts of data -- Challenges of deep learning for big data -- Challenges of deep learning due to massive volumes of data (first V) -- Challenges of deep learning from a high variety of data (second V) -- Challenges of deep learning from a high velocity of data (third V) -- Challenges of deep learning to maintain the veracity of data (fourth V) -- Distributed deep learning and Hadoop -- Map-Reduce -- Iterative Map-Reduce -- Yet Another Resource Negotiator (YARN) -- Important characteristics for distributed deep learning design -- Deeplearning4j - an open source distributed framework for deep learning -- Major features of Deeplearning4j -- Summary of functionalities of Deeplearning4j -- Setting up Deeplearning4j on Hadoop YARN -- Getting familiar with Deeplearning4j -- Integration of Hadoop YARN and Spark for distributed deep learning -- Rules to configure memory allocation for Spark on Hadoop YARN -- Summary -- Chapter 3: Convolutional Neural Network -- Understanding convolution -- Background of a CNN -- Architecture overview -- Basic layers of CNN Importance of depth in a CNN -- Convolutional layer -- Sparse connectivity -- Improved time complexity -- Parameter sharing -- Improved space complexity -- Equivariant representations -- Choosing the hyperparameters for Convolutional layers -- Depth -- Stride -- Zero-padding -- Mathematical formulation of hyperparameters -- Effect of zero-padding -- ReLU (Rectified Linear Units) layers -- Advantages of ReLU over the sigmoid function -- Pooling layer -- Where is it useful, and where is it not? -- Fully connected layer -- Distributed deep CNN -- Most popular aggressive deep neural networks and their configurations -- Training time - major challenges associated with deep neural networks -- Hadoop for deep CNNs -- Convolutional layer using Deeplearning4j -- Loading data -- Model configuration -- Training and evaluation -- Summary -- Chapter 4: Recurrent Neural Network -- What makes recurrent networks distinctive from others? -- Recurrent neural networks(RNNs) -- Unfolding recurrent computations -- Advantages of a model unfolded in time -- Memory of RNNs -- Architecture -- Backpropagation through time (BPTT) -- Error computation -- Long short-term memory -- Problem with deep backpropagation with time -- Long short-term memory -- Bi-directional RNNs -- Shortfalls of RNNs -- Solutions to overcome -- Distributed deep RNNs -- RNNs with Deeplearning4j -- Summary -- Chapter 5: Restricted Boltzmann Machines -- Energy-based models -- Boltzmann machines -- How Boltzmann machines learn -- Shortfall -- Restricted Boltzmann machine -- The basic architecture -- How RBMs work -- Convolutional Restricted Boltzmann machines -- Stacked Convolutional Restricted Boltzmann machines -- Deep Belief networks -- Greedy layer-wise training -- Distributed Deep Belief network -- Distributed training of Restricted Boltzmann machines -- Distributed training of Deep Belief networks Distributed back propagation algorithm -- Performance evaluation of RBMs and DBNs -- Drastic improvement in training time -- Implementation using Deeplearning4j -- Restricted Boltzmann machines -- Deep Belief networks -- Summary -- Chapter 6: Autoencoders -- Autoencoder -- Regularized autoencoders -- Sparse autoencoders -- Sparse coding -- Sparse autoencoders -- The k-Sparse autoencoder -- How to select the sparsity level k -- Effect of sparsity level -- Deep autoencoders -- Training of deep autoencoders -- Implementation of deep autoencoders using Deeplearning4j -- Denoising autoencoder -- Architecture of a Denoising autoencoder -- Stacked denoising autoencoders -- Implementation of a stacked denoising autoencoder using Deeplearning4j -- Applications of autoencoders -- Summary -- Chapter 7: Miscellaneous Deep Learning Operations using Hadoop -- Distributed video decoding in Hadoop -- Large-scale image processing using Hadoop -- Application of Map-Reduce jobs -- Natural language processing using Hadoop -- Web crawler -- Extraction of keyword and module for natural language processing -- Estimation of relevant keywords from a page -- Summary -- References -- Index

     
    Export in Literaturverwaltung   RIS-Format
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    Hinweise zum Inhalt
    Volltext (lizenzpflichtig)
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781787121232
    Schlagworte: Apache Hadoop; Electronic books
    Umfang: 1 online resource (200 pages)
  3. Deep Learning with Hadoop
    Autor*in: Dev, Dipayan
    Erschienen: 2017; © 2017
    Verlag:  Packt Publishing, Birmingham

    Standort:
    Universitätsbibliothek Erlangen-Nürnberg, Hauptbibliothek
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe
    Link zum Verbundkatalog:
    Bibliotheksverbund Bayern (BVB)
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781787121232
    Auflage/Ausgabe: 1st ed
    Schlagworte: Apache Hadoop
    Umfang: 1 online resource (200 pages)
    Bemerkung(en):

    Description based on publisher supplied metadata and other sources