Відео

02 ML Diffrent tools and application of ML

01 ML Introduction

Lecture 12 Problems 2

Lecture 13 Discriminant Functions 2021 09 16 at 02 47 GMT 7

lect 14 Minimum Distance Classifier

Lecture 15 Minimum Distance Classifier 2021 09 21 at 02 32 GMT 7

Lecture 16 3 class problem 2021 09 23 at 02 35 GMT 7

Lecture 17 AINN Single Layer discriminant function

Lecture 19 Perceptron Convergence Part1

Lecture 20 Perceptron Convergence Part2

Lecture 18 AINN Single Layer discriminant Dunction

Lecture 21 Multi category Perceptron Training

Lecture 25 Generalized Delta LR Derivation

Lect 11 WTA cntd 2021 09 07 at 02 32 GMT 7

Lecture 09 Learning rules contd 2021 08 31 at 01 34 GMT 7

lecture 10 WTA 2021 09 02 at 02 33 GMT 7

Lecture 08 A Activation functions

lect07 Learning Rules cntd.

lect06 Mculloch-Pitts Model ,and Learning Rules Part 2

Lect05 Mc-Culloch - Pitts Model and Learning Rules- Part-1

Lect 03 AI NN A 2021 08 12 at 23 43 GMT 7

Lect 02 Introduction AI NN 2021 08 10 at 02 34 GMT 7

Lect 04 Introduction AI NN A 2021 08 17 at 02 39 GMT 7

Lecture 22 Problem XOR

Lecture 26 Back Propagation Algorithm Derivation Part- 1

Lecture 27 Back Propagation Algorithm Derivation Part- 2

Lecture 29 Factors affecting BP training

Lecture 30 Size of Network , Functional Link Networks 2021 11 22 at 01 32 GMT 8

Lecture 32 Linear Associator