Discover the essential building blocks of the most common forms of deep belief networks. At
each step this book provides 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. The first of three in a series on C++ and CUDA C deep learning and belief nets Deep
Belief Nets in C++ and CUDA C: Volume 1 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. As such
you'll see that a typical deep belief net can learn to recognize complex patterns by optimizing
millions of parameters yet this model can still be resistant to overfitting. All the routines
and algorithms presented in the book are available in the code download which also contains
some libraries of related routines. What You Will Learn Employ deep learning using C++ and CUDA
C Work with supervised feedforward networks Implement restricted Boltzmann machines Use
generative samplings Discover why these are important 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.
