| 书目名称 | Deep Belief Nets in C++ and CUDA C: Volume 2 | | 副标题 | Autoencoding in the | | 编辑 | Timothy Masters | | 视频video | | | 概述 | A practical book with source code and algorithms on deep learning with C++ and CUDA C.Second of three books in a series on C++ and CUDA C deep learning and belief nets.Author is an authority on numeri | | 图书封面 |  | | 描述 | 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.Car | | 出版日期 | Book 2018 | | 关键词 | C++; CUDA C; AI; artificial intel; machine learning; deep learning; programming; algorithms; numerical; compu | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-3646-8 | | isbn_softcover | 978-1-4842-3645-1 | | isbn_ebook | 978-1-4842-3646-8 | | copyright | Timothy Masters 2018 |
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发表于 2025-3-21 18:28:19
