Deep Belief Nets in C++ and CUDA C: Volume 3: Convolutional Nets

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(as of Dec 27,2024 07:12:16 UTC – Details)

ASIN ‏ : ‎ B07F8MFW7Q
Publisher ‏ : ‎ Apress; 1st ed. edition (July 4, 2018)
Publication date ‏ : ‎ July 4, 2018
Language ‏ : ‎ English
File size ‏ : ‎ 2685 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 190 pages

Deep Belief Nets in C++ and CUDA C: Volume 3: Convolutional Nets

In this third volume of our series on deep belief nets, we will delve into the world of convolutional nets. Convolutional nets are a type of deep neural network that are particularly well-suited for image recognition tasks.

In this post, we will explore how to implement convolutional nets in C++ and CUDA C. We will discuss the architecture of convolutional nets, including convolutional layers, pooling layers, and fully connected layers. We will also cover how to train convolutional nets using backpropagation and stochastic gradient descent.

Additionally, we will demonstrate how to optimize the performance of convolutional nets by leveraging the parallel computing capabilities of CUDA C. By offloading computations to the GPU, we can significantly accelerate training and inference for convolutional nets.

Overall, this post will provide a comprehensive overview of convolutional nets and how to implement them in C++ and CUDA C. Stay tuned for more deep learning insights in future volumes of our series!
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