Deep Belief Nets in C++ and CUDA C: Volume 3 Convolutional Nets
Deep Belief Nets in C++ and CUDA C: Volume 3 Convolutional Nets by Timothy Masters, published by Apress on July 5, 2018, is a comprehensive guide that delves into the architecture and functionality of convolutional networks, a significant type of deep belief network. This 176-page book presents the essential building blocks of these models, emphasizing their resemblance to human cognitive processes and their effectiveness in image processing applications. Readers will find a structured approach that includes intuitive motivation, key equations, and well-commented code designed for both modern CPUs and CUDA-capable graphics cards.
In this edition, the author provides practical insights into building deep feedforward networks, utilizing locally connected layers, pooling layers, and softmax outputs. The book covers essential programming algorithms, multi-threaded gradient computations, and memory management, along with CUDA code implementations for core computations. Additionally, readers can access source code for all routines and the CONVNET program, which facilitates exploration of convolutional nets and related case studies. This resource is tailored for individuals with a foundational understanding of neural networks and some programming experience, particularly in C++ and CUDA C.
Official synopsis Publisher
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.
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