Book description
- 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
Product information
- Title: Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain
- Author(s):
- Release date: May 2018
- Publisher(s): Apress
- ISBN: 9781484236468
You might also like
book
Deep Belief Nets in C++ and CUDA C: Volume 3: Convolutional Nets
Discover the essential building blocks of a common and powerful form of deep belief network: convolutional …
book
View-based 3-D Object Retrieval
Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of …
book
The TAB Book of Arduino Projects: 36 Things to Make with Shields and Proto Shields
The ultimate collection of DIY Arduino projects! In this easy-to-follow book, electronics guru Simon Monk shows …
book
Minimalist Mobile Robotics
Rather than using traditional artificial intelligence techniques, which are ineffective when applied to the complexities of …