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  • Online Resource  (6)
  • Masters, Timothy  (3)
  • Vohra, Deepak  (3)
  • [Place of publication not identified] : Apress  (6)
  • Electronic books ; local  (6)
  • 1
    Online Resource
    Online Resource
    Deep Belief Nets in C++ and CUDA C (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484236468
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; C++ (Computer program language) ; Machine learning ; Electronic books ; local ; Electronic books
    Abstract: Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You'll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for preprocessing time series and image data. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, you'll learn a method for embedding class information in the input layer of a restricted Boltzmann machine. This facilitates generative display of samples from individual classes rather than the entire data distribution. The ability to see the features that the model has learned for each class separately can be invaluable. At each step this book provides you with intuitive motivation, a summary of the most important equations relevant to the topic, and highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. What You'll Learn Code for deep learning, neural networks, and AI using C++ and CUDA C Carry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and more Use the Fourier Transform for image preprocessing Implement autoencoding via activation in the complex domain Work with algorithms for CUDA gradient computation Use the DEEP operating manual Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
    Note: Description based on online resource; title from cover (Safari, viewed June 22, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Deep belief nets in C++ and CUDA C (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484237212
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; C++ (Computer program language) ; Electronic books ; local ; Electronic books
    Abstract: Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download. What You Will Learn Discover convolutional nets and how to use them Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs Master the various programming algorithms required Carry out multi-threaded gradient computations and memory allocations for this threading Work with CUDA code implementations of all core computations, including layer activations and gradient calculations Make use of the CONVNET program and manual to explore convolutional nets and case studies Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
    Note: Description based on online resource; title from cover (Safari, viewed August 7, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Deep Belief Nets in C++ and CUDA C (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484235911
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; C++ (Computer program language) ; Electronic books ; local ; Electronic books
    Abstract: Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you'll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
    Note: Description based on online resource; title from cover (Safari, viewed May 21, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
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  • 4
    Online Resource
    Online Resource
    Docker management design patterns : swarm mode on Amazon Web Services (2017)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Amazon Web Services (Firm) ; Cloud computing ; Virtual computer systems ; Application software ; Development ; Software patterns ; Electronic books ; Electronic books ; local
    Abstract: Master every aspect of orchestrating/managing Docker including creating a Swarm, creating services, using mounts, scheduling, scaling, resource management, rolling updates, load balancing, high availability, logging and monitoring, using multiple zones, and networking. This book also discusses the managed services for Docker Swarm: Docker for AWS and Docker Cloud Swarm mode. Docker Management Design Patterns explains how to use Docker Swarm mode with Docker Engine to create a distributed Docker container cluster and how to scale a cluster of containers, schedule containers on specific nodes, and mount a volume. This book is based on the latest version of Docker (17.0x). You will learn to provision a Swarm on production-ready AWS EC2 nodes, and to link Docker Cloud to Docker for AWS to provision a new Swarm or connect to an existing Swarm. Finally, you will learn to deploy a Docker Stack on Docker Swarm with Docker Compose. What You'll Learn Apply Docker management design patterns Use Docker Swarm mode and other new features Create and scale a Docker service Use mounts including volumes Configure scheduling, load balancing, high availability, logging and monitoring, rolling updates, resource management, and networking Use Docker for AWS managed services including a multi-zone Swarm Build Docker Cloud managed services in Swarm mode Who This Book Is For Docker admins, Docker application developers, and container as a service (CAAS) developers. Some prerequisite knowledge of Linux and Docker is required. Apress Pro Docker is recommended as a companion to this book.
    Note: Description based on online resource; title from cover (Safari, viewed November 28, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781484229736/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
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  • 5
    Online Resource
    Online Resource
    Practical Hadoop ecosystem : a definitive guide to Hadoop-related frameworks and tools (2016)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Apache Hadoop ; Database management ; Electronic books ; Electronic books ; local
    Abstract: Learn how to use the Apache Hadoop projects, including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout, and Apache Solr. From setting up the environment to running sample applications each chapter in this book is a practical tutorial on using an Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects, MapReduce and HDFS, and none discusses the other Apache Hadoop ecosystem projects and how they all work together as a cohesive big data development platform. What You Will Learn: Set up the environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5 Run a MapReduce job Store data with Apache Hive, and Apache HBase Index data in HDFS with Apache Solr Develop a Kafka messaging system Stream Logs to HDFS with Apache Flume Transfer data from MySQL database to Hive, HDFS, and HBase with Sqoop Create a Hive table over Apache Solr Develop a Mahout User Recommender System Who This Book Is For: Apache Hadoop developers. Pre-requisite knowledge of Linux and some knowledge of Hadoop is required.
    Note: Description based on online resource; title from cover (Safari, viewed November 22, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781484221990/?ar
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  • 6
    Online Resource
    Online Resource
    Apache HBase primer (2016)
    [Place of publication not identified] : Apress
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Apache Hadoop ; Databases ; Open source software ; Electronic books ; Electronic books ; local
    Abstract: Learn the fundamental foundations and concepts of the Apache HBase (NoSQL) open source database. It covers the HBase data model, architecture, schema design, API, and administration. Apache HBase is the database for the Apache Hadoop framework. HBase is a column family based NoSQL database that provides a flexible schema model. What You'll Learn Work with the core concepts of HBase Discover the HBase data model, schema design, and architecture Use the HBase API and administration Who This Book Is For Apache HBase (NoSQL) database users, designers, developers, and admins.
    Note: Description based on online resource; title from cover (Safari, viewed February 1, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781484224243/?ar
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    Online Resource
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