000			a
999			_c31712 _d31712
008			230413b xxu||||| |||| 00| 0 eng d
020			_a9780000988546
082			_a006.31 _bGRA
100			_aGraupe, Daniel
245			_aDeep learning neural networks : design and case studies
260			_bWorld Scientific Publishing, _c2022 _aSingapore :
300			_axvi, 263 p. ; _bill., _c25 cm
365			_b1295.00 _cINR _d01
504			_aIncludes bibliographical references and index.
520			_aDeep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance. This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.
650			_aART neural network
650			_a Back propogation
650			_a Boltzmmann machine
650			_a CNN applications
650			_aConvolutional Neural networks
650			_aData analysis
650			_a Deep learning neural networks
650			_aExcitory neuron
650			_aFingerprint recognition
650			_aGibbs-Boltzmann-distribution
650			_aHopfield neural network
650			_a Input matrix
650			_a K-lines
650			_aLAMSTAR network
650			_aLink-weights
650			_a Neocognition
650			_aOutput layer
650			_aRestricted Boltzmann machine
650			_aSpeech recognition
650			_aSupport vector machines
650			_aWinning neuron
942			_2ddc _cBK