000			01369nam a22002297a 4500
999			_c686 _d686
005			20200826163655.0
008			190107b ||||| |||| 00| 0 eng d
020			_a9789332570313
040			_c0
082			_a004.89 _bHAY
100			_aHaykin, Simon
245			_aNeural networks and learning machines _cSimon Haykin
250			_a3/e
260			_aNoida _bPearson _c2016
300			_a909p. _bill., _c24cm
505			_tRosenblatt's perceptron -- _tModel building through regression -- _tThe least-mean-square algorithm -- _tMultilayer perceptrons -- _tKernel methods and radial-basis function networks -- _tSupport vector machines -- _tRegularization theory -- _tPrincipal-components analysis -- _tSelf-organizing maps -- _tInformation-theoretic learning models -- _tStochastic methods rooted in statistical mechanics -- _tDynamic programming -- _tNeurodynamics -- _tBayseian filtering for state estimation of dynamic systems -- _tDynamically driven recurrent networks.
520			_aUsing a wealth of case studies to illustrate the real-life, practical applications of neural networks, this state-of-the-art text exposes students to many facets of Neural Networks
650			_aNeural networks (Computer science)
650			_aNeural networks (Computer science) -- Problems, exercises, etc.
650			_a Lernendes System
942			_2ddc _cBK