商品描述
Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how 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. These models are especially useful for image processing applications.
At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents 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 executable CONVNET program which implements these algorithms, are available for free download.
What You Will Learn
- Discover convolutional nets and how to use them
- Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
- Master the various programming algorithms required
- Carry out multi-threaded gradient computations and memory allocations for this threading
- Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
- Make use of the CONVNET program and manual to explore convolutional nets and case studies
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)
探索一種常見且強大的深度信念網路的基本構建塊：卷積網路。本書向您展示這些優雅模型的結構與人類大腦的結構相比，與傳統神經網路更為接近；它們擁有一種能夠從更簡單的原始概念中學習抽象概念的「思考過程」。這些模型在圖像處理應用中尤其有用。
在每一步中，《Deep Belief Nets in C++ and CUDA C: Volume 3》提供直觀的動機、與主題相關的最重要方程式的摘要，並以高度註解的代碼結束，這些代碼適用於現代 CPU 的多線程計算以及在具備 CUDA 功能的顯示卡的計算機上進行大規模並行處理。本書中所有例程的源代碼以及實現這些算法的可執行 CONVNET 程式均可免費下載。
您將學到的內容：
- 探索卷積網路及其使用方法
- 使用局部連接層、池化層和 softmax 輸出構建深度前饋網路
- 精通所需的各種編程算法
- 執行多線程梯度計算和此線程的記憶體分配
- 使用 CUDA 代碼實現所有核心計算，包括層激活和梯度計算
- 利用 CONVNET 程式和手冊探索卷積網路和案例研究
本書適合對神經網路有基本了解並具備一定編程經驗的讀者，雖然建議具備一些 C++ 和 CUDA C 的知識。
