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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)
發現一種常見且強大的深度信念網絡（Deep Belief Net）形式的基本構建塊：自編碼器（autoencoder）。您將把這個主題擴展到複數域，以應用於信號和圖像處理，超越當前的使用情境。《Deep Belief Nets in C++ and CUDA C: Volume 2》還涵蓋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合輸入到複數域自編碼器的複數域預測器。最後，您將學習一種將類別信息嵌入限制玻爾茲曼機（restricted Boltzmann machine）輸入層的方法。這使得從單個類別而非整個數據分佈生成樣本的顯示成為可能。能夠分別查看模型為每個類別學習到的特徵是非常寶貴的。
在每一步中，本書提供直觀的動機、與主題相關的最重要方程式的摘要，以及針對現代 CPU 的線程計算和具備 CUDA 功能的顯示卡的高註解代碼。
您將學到的內容：
- 使用 C++ 和 CUDA C 進行深度學習、神經網絡和人工智慧的代碼
- 使用簡單變換、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
- 通過在複數域中的激活實現自編碼
- 使用 CUDA 梯度計算的算法
- 使用 DEEP 操作手冊
本書適合對象：
對神經網絡有至少基本知識並具備一些編程經驗的人，雖然建議具備一些 C++ 和 CUDA C 的知識。
