Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain

Timothy Masters

  • 出版商: Apress
  • 出版日期: 2018-06-11
  • 售價: $2,020
  • 貴賓價: 9.5$1,919
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Paperback
  • ISBN: 1484236459
  • ISBN-13: 9781484236451
  • 相關分類: C++ 程式語言CUDA
  • 海外代購書籍(需單獨結帳)

買這商品的人也買了...

相關主題

商品描述

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
  • 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.

商品描述(中文翻譯)

本書介紹了一種常見且強大的深度信念網絡結構的基本構建塊:自編碼器。通過將其擴展到複雜領域,您可以將此主題應用於信號和圖像處理應用中。《C++和CUDA C中的深度信念網絡:第2卷》還介紹了幾種用於預處理時間序列和圖像數據的算法。這些算法專注於創建適用於複雜領域自編碼器的複雜領域預測器。最後,您將學習一種將類別信息嵌入到受限玻爾茨曼機的輸入層的方法。這有助於從單個類別而不是整個數據分布中生成樣本。能夠單獨查看模型為每個類別學到的特徵可能非常寶貴。

本書在每個步驟中提供直觀的動機,總結與該主題相關的最重要的方程式,以及用於現代CPU上的線程計算和支持CUDA的顯示卡上的大規模並行處理的高度註釋代碼。

本書的學習重點包括:
- 使用C++和CUDA C進行深度學習、神經網絡和人工智能的代碼
- 使用簡單的轉換、傅立葉變換、Morlet小波等進行信號預處理
- 使用傅立葉變換進行圖像預處理
- 在複雜領域中實現自編碼器
- 使用CUDA梯度計算算法
- 使用DEEP操作手冊

本書適合具有基本神經網絡知識和一些編程經驗的讀者,儘管建議具備一些C++和CUDA C的知識。