Deep Belief Nets in C++ and CUDA C: Volume 3: Convolutional Nets

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
subject_code
UYQ
gpsr_safety_attestation
true
target_audience
General/trade
is_adult_product
false
edition_number
1
binding
paperback
edition
First
series_number
3
MPN
N/A
batteries_required
false
manufacturer
Apress
volume
3
Brand
Apress
number_of_items
1
pages
188
genre
Artificial intelligence
publication_date
2018-07-05T00:00:01Z
unspsc_code
55101500
batteries_included
false
ISBN
9781484237205
Category

About this product

Product Identifiers

Publisher
Apress L. P.
ISBN-10
148423720X
ISBN-13
9781484237205
eBay Product ID (ePID)
25038367451

Product Key Features

Number of Pages
Xii, 176 Pages
Publication Name
Deep Belief Nets in C++ and CUDA C: Volume 3 : Convolutional Nets
Language
English
Publication Year
2018
Subject
Programming Languages / C++, Intelligence (Ai) & Semantics, Neural Networks, Databases / General, Compilers
Type
Textbook
Author
Timothy Masters
Subject Area
Computers
Format
Trade Paperback

Dimensions

Item Weight
16 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Number of Volumes
1 vol.
Illustrated
Yes
Synopsis
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.
LC Classification Number
Q334-342

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