Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks (Paperback)

Timothy Masters

  • 出版商: CreateSpace Independ
  • 出版日期: 2015-02-11
  • 售價: $1,960
  • 貴賓價: 9.5$1,862
  • 語言: 英文
  • 頁數: 244
  • 裝訂: Paperback
  • ISBN: 1507751478
  • ISBN-13: 9781507751473
  • 相關分類: C++ 程式語言CUDA
  • 無法訂購

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商品描述

Deep belief nets are one of the most exciting recent developments in artificial intelligence. 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. 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. This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text 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. Source code for all routines presented in the book, and the DEEP program which implements these algorithms, are available for free download from the author’s website.

商品描述(中文翻譯)

深度信念網絡是人工智慧領域中最令人興奮的最新發展之一。這些優雅模型的結構比傳統神經網絡更接近人類大腦,它們具有一種能夠從更簡單的基本元素中學習抽象概念的“思考過程”。一個典型的深度信念網絡可以通過億萬參數的優化來學習識別複雜模式,但這種模型仍然可以抵抗過度擬合。本書介紹了最常見形式的深度信念網絡的基本構建塊。在每一步中,本書提供直觀的動機,總結了與該主題相關的最重要的方程式,並以高度註釋的代碼結束,該代碼可在現代CPU上進行線程計算,也可在具有CUDA兼容顯示卡的計算機上進行大規模並行處理。本書中介紹的所有例程的源代碼以及實現這些算法的DEEP程序都可以從作者的網站免費下載。