Deep Belief Nets in C++ and CUDA C: Volume II: Autoencoding in the Complex Domain (Paperback)

Timothy Masters

  • 出版商: CreateSpace Independ
  • 出版日期: 2015-06-24
  • 售價: $1,850
  • 貴賓價: 9.5$1,758
  • 語言: 英文
  • 頁數: 242
  • 裝訂: Paperback
  • ISBN: 1514365995
  • ISBN-13: 9781514365991
  • 相關分類: C++ 程式語言CUDA
  • 立即出貨(限量) (庫存=1)

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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 a common and powerful form of deep belief net: the autoencoder. Volume II takes this topic beyond current usage by extending it to the complex domain, which is useful for many signal and image processing applications. Several algorithms for preprocessing time series and image data are also presented. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, this book provides 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 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能領域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,但這種模型仍然能夠抵抗過度擬合。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。第二卷將這個主題擴展到複雜領域,這對於許多信號和圖像處理應用非常有用。書中還介紹了幾種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域自編碼器的複雜域預測器。最後,本書提供了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。能夠分別查看模型為每個類別學到的特徵是非常寶貴的。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。