本文為捷克布拉格查理大學（作者：Bc. Ján Vojt）的碩士論文，共104頁。

深度神經網路是一種有效且通用的模型，能夠完成各種各樣的任務。本文主要研究了三種不同型別的深度神經網路——多層感知器、卷積神經網路和深度置信網路。所有討論的網路模型都是在並行硬體上實現的，並且針對網路體系結構及其引數的各種選擇進行了全面測試。作者對所實現系統的體系結構選擇和優化都進行了詳細的文件記錄，最終的系統測試結果驗證了該框架下的執行效率優勢。本文的另一個重要部分還對支援深度神經網路的其它現有框架進行了額外的測試，對比測試表明，我們提出的設計框架優於多層感知器和卷積神經網路。

深度置信網路的效能稍微優於多達1000個隱藏神經元的RBM（Restricted Boltzmann Machine，受限玻爾茲曼機）層，但是對於更健壯的RBM層，與測試的競爭框架相比，其效能明顯較低。

Deep neural networks represent an effectiveand universal model capable of solving a wide variety of tasks. This thesis isfocused on three different types of deep neural networks – the multilayerperceptron, the convolutional neural network, and the deep belief network. Allof the discussed network models are implemented on parallel hardware, andthoroughly tested for various choices of the network architecture and itsparameters. The implemented system is accompanied by a detailed documentationof the architectural decisions and proposed optimizations. The efficiency ofthe implemented framework is confirmed by the results of the performed tests. Asignificant part of this thesis represents also additional testing of otherexisting frameworks which support deep neural networks. This comparisonindicates superior performance to the tested rival frameworks of multilayerperceptrons and convolutional neural networks. The deep belief networkimplementation performs slightly better for RBM layers with up to 1000 hiddenneurons, but has a noticeably inferior performance for more robust RBM layerswhen compared to the tested rival framework.

1 引言
2 人工神經網路
3 具體實現
4 實驗與測試
5 結論

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