Computer Architecture Flynn S Classification Exam Study Material for Gate ECE

Power and Taylorâs Series of Complex Numbers	lec18	00:42 to 05:45	PDF	VIDEO
Power and Taylorâs Series of Complex Numbers	lec18	10:33 to 14:29	PDF	VIDEO
Tayorâs, Laurent Series of f(z) and Singularities	lec20	04:02 to 08:26	PDF	VIDEO
Tayorâs, Laurent Series of f(z) and Singularities	lec20	26:52 to 30:42	PDF	VIDEO
Tayorâs, Laurent Series of f(z) and Singularities	lec20	36:54 to 41:14	PDF	VIDEO
Classification of Singularities, Residue and Residue Theorem	lec21	07:36 to 11:58	PDF	VIDEO
Classification of Singularities, Residue and Residue Theorem	lec21	11:58 to 15:36	PDF	VIDEO
Classification of Singularities, Residue and Residue Theorem	lec21	24:54 to 27:44	PDF	VIDEO
Classification of Singularities, Residue and Residue Theorem	lec21	32:58 to 37:04	PDF	VIDEO

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Linear Discriminant functions for Multi-Class Case; Multi-Class Logistic Regression	lec19	08:52 to 10:35	PDF	VIDEO
Feedforward networks for Classification and Regression; Back propagation in Practice	lec29	05:32 to 06:31	PDF	VIDEO
Feedforward networks for Classification and Regression; Back propagation in Practice	lec29	11:50 to 17:04	PDF	VIDEO
Feedforward networks for Classification and Regression; Back propagation in Practice	lec29	22:00 to 23:22	PDF	VIDEO
Feedforward networks for Classification and Regression; Back propagation in Practice	lec29	23:47 to 26:21	PDF	VIDEO
Feedforward networks for Classification and Regression; Back propagation in Practice	lec29	27:09 to 29:16	PDF	VIDEO
SVM formulation with slack variables; non-linear SVM classifiers	lec33	46:52 to 50:33	PDF	VIDEO
SVM formulation with slack variables; non-linear SVM classifiers	lec33	52:34 to 55:54	PDF	VIDEO
Kernel Functions for non-linear SVMs; Mercer and positive definite Kernels	lec34	14:35 to 16:47	PDF	VIDEO
Kernel Functions for non-linear SVMs; Mercer and positive definite Kernels	lec34	55:43 to 57:01	PDF	VIDEO