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Titlebook: Deep Belief Nets in C++ and CUDA C: Volume 1; Restricted Boltzmann Timothy Masters Book 2018 Timothy Masters 2018 C++.CUDA C.deep learning.

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發(fā)表于 2025-3-21 16:55:49 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Deep Belief Nets in C++ and CUDA C: Volume 1
副標(biāo)題Restricted Boltzmann
編輯Timothy Masters
視頻videohttp://file.papertrans.cn/265/264522/264522.mp4
概述Master deep learning with C++ and CUDA C.Utilize restricted Boltzmann machines.Work with supervised feedforward networks
圖書封面Titlebook: Deep Belief Nets in C++ and CUDA C: Volume 1; Restricted Boltzmann Timothy Masters Book 2018 Timothy Masters 2018 C++.CUDA C.deep learning.
描述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 theroutines 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 Boltzm
出版日期Book 2018
關(guān)鍵詞C++; CUDA C; deep learning; Boltzmann; machine; AI; artificial intelligence; numerical; algorithms; CV; comput
版次1
doihttps://doi.org/10.1007/978-1-4842-3591-1
isbn_softcover978-1-4842-3590-4
isbn_ebook978-1-4842-3591-1
copyrightTimothy Masters 2018
The information of publication is updating

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發(fā)表于 2025-3-21 22:55:02 | 只看該作者
板凳
發(fā)表于 2025-3-22 04:08:22 | 只看該作者
地板
發(fā)表于 2025-3-22 05:45:27 | 只看該作者
Restricted Boltzmann Machines,beat. Also, the numerous technical papers by Geoffrey Hinton cover specific aspects of RMBs in glorious detail. Finally, deeplearning.net is an incredible resource. Because of this wealth of material, I will avoid unnecessary duplication. This chapter will be limited to an outline of the essentials
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發(fā)表于 2025-3-22 10:46:05 | 只看該作者
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發(fā)表于 2025-3-22 14:12:37 | 只看該作者
al 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 pro
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發(fā)表于 2025-3-22 17:33:38 | 只看該作者
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2. Notions from homological algebra,ible resource. Because of this wealth of material, I will avoid unnecessary duplication. This chapter will be limited to an outline of the essentials of RBMs, in other words, the information necessary to understand and use the programs presented here.
9#
發(fā)表于 2025-3-23 04:19:15 | 只看該作者
Supervised Feedforward Networks,h patterns have been found does training switch to supervised mode. However, because supervised training algorithms are easier to understand than the usual unsupervised algorithms, you will begin your study of deep belief nets with supervised training.
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發(fā)表于 2025-3-23 09:22:04 | 只看該作者
Restricted Boltzmann Machines,ible resource. Because of this wealth of material, I will avoid unnecessary duplication. This chapter will be limited to an outline of the essentials of RBMs, in other words, the information necessary to understand and use the programs presented here.
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