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.
