000			01729nam a22001937a 4500
999			_c64860 _d64860
008			200430b2018 ||||| |||| 00| 0 eng d
020			_a9781484240205
082			_a006.32 MAS-T
100			_aMasters, Timothy
245			_aDeep belief nets in C++ and CUDA C : volume 1 - restricted Boltzmann machines and supervised feedforward networks / _cTimothy Masters
260			_aNY _bApress _c2018
300			_a219 p.
365			_aINR _b609.00
500			_aDiscover 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.
650			_aNeural networks (Computer science)
650			_aC++ (Computer program language)
650			_aProgramming languages (Electronic computers)
650			_aCUDA (Computer architecture)