| Back | Neural Networks and Applications
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Author: Prof. S. Sengupta |
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Lectures |
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Titles |
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1 |
Introduction to Artificial Neural Networks | Download | |
2 |
Artificial Neural Model and Linear Regression | Download | |
3 |
Gradient Descent Algorithm |
Download | |
4 |
Nonlinear Activation Units and Learning Mechanics | Download | |
5 |
Learning Mechanisms - Hebbian, Competitive, Boltzmann | Download | |
6 |
Associative Memory | Download | |
7 |
Associative Memory Model | Download | |
8 |
Conditions for Perfect recall in Associative Memory | Download | |
9 |
Statistical Aspects of Learning | Download | |
10 |
V.C. Deimensions : Typical Examples | Download | |
11 |
Importance of V.C. Deimensions Structural Risk Minimization | Download | |
12 |
Single Layer Perceptions | Download | |
13 |
Unconstrained Optimization : Gauss - Newton's method | Download | |
14 |
Linear Least Square Filters | Download | |
15 |
Least Mean Square Algorithm | Download | |
16 |
Perceptron Convergence Theorem | Download | |
17 |
Bayes Classifiers & Perceptron : An Analogy | Download | |
18 |
Bayes Classifiers for Gaussion Distribution | Download | |
19 |
Back Propagation Algorithm | Download | |
20 |
Practical Consideration in Back Propagation Algorithm | Download | |
21 |
Solution of Non-Linearly separate problems using MLP | Download | |
22 |
Heuristics For Back - Propagation | Download | |
23 |
Multi-class classification using Multi-Layered Perceptrons (MLP) | Download | |
24 |
Radial Basis Function (RBF) Networks : Cover's Theorem | Download | |
25 |
Radial Basis Function (RBF) Networks : Separability & Interpolation | Download | |
26 |
Radial Basis Function as ill - posed surface reconstruction | Download | |
27 |
Solution of Regularization Equation : Green's Function | Download | |
28 |
Use of Green's Function in Regularization Networks | Download | |
29 |
Regularisation Networks and Generalised RBF | Download | |
30 |
Comparison between MLP and RBF | Download | |
31 |
Learning mechanisms in RBF | Download | |
32 |
Intro to Principal components and analysis | Download | |
33 |
Dimensionality reduction using PCA | Download | |
34 |
Hebbian - based Principal component analysis | Download | |
35 |
Introduction to self organising Maps (SOM) | Download | |
36 |
Co-operative and adaptive processes in SOM | Download | |
37 |
Vector- Quantisation using SOM | Download | |