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Neural Networks and Applications

Neural Networks and Applications. Instructor: Prof. Somnath Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. This course covers lessons in artificial neural networks, associative memory, single layer perceptrons, back propagation algorithm, learning mechanisms in Radial Basis Function (RBF) and vector-quantization using Self-Organizing Maps (SOM). (from nptel.ac.in)

Lecture 36 - Cooperative and Adaptive Processes in Self-Organizing Maps (SOM)

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Lecture 01 - Introduction to Artificial Neural Networks

Lecture 02 - Artificial Neural Model and Linear Regression

Lecture 03 - Gradient Descent Algorithm

Lecture 04 - Nonlinear Activation Units and Learning Mechanisms

Lecture 05 - Learning Mechanisms - Hebbian, Competitive, Boltzmann

Lecture 06 - Associative Memory

Lecture 07 - Associative Memory Model

Lecture 08 - Condition for Perfect Recall in Associative Memory

Lecture 09 - Statistical Aspects of Learning

Lecture 10 - VC Dimensions: Typical Examples

Lecture 11 - Importance of VC Dimensions: Structural Risk Minimization

Lecture 12 - Single Layer Perceptions

Lecture 13 - Unconstrained Optimization: Gauss-Newton's Method

Lecture 14 - Linear Least Square Filters

Lecture 15 - Least Mean Squares Algorithm

Lecture 16 - Perceptron Convergence Theorem

Lecture 17 - Bayes Classifier and Perceptron: An Analogy

Lecture 18 - Bayes Classifier for Gaussian Distribution

Lecture 19 - Backpropagation Algorithm

Lecture 20 - Practical Consideration in Backpropagation Algorithm

Lecture 21 - Solution of Nonlinearly Separable Problems using Multilayer Perceptron (MLP)

Lecture 22 - Heuristics for Backpropagation

Lecture 23 - Multiclass Classification using Multilayered Perceptrons

Lecture 24 - Radial Basis Function Networks: Cover's Theorem

Lecture 25 - Radial Basis Function Networks: Separability and Interpolation

Lecture 26 - Radial Basis Function as Ill-Posed Surface Reconstruction

Lecture 27 - Solution of Regularization Equation: Green's Function

Lecture 28 - Use of Green's Function in Regularization Networks

Lecture 29 - Regularization Networks and Generalized Radial Basis Function (RBF)

Lecture 30 - Comparison between MLP and RBF

Lecture 31 - Learning Mechanisms in Radial Basis Function (RBF)

Lecture 32 - Introduction to Principal Components and Analysis

Lecture 33 - Dimensionality Reduction using Principal Components Analysis (PCA)

Lecture 34 - Hebbian-Based Principal Components Analysis

Lecture 35 - Introduction to Self-Organizing Maps

Lecture 36 - Cooperative and Adaptive Processes in Self-Organizing Maps (SOM)

Lecture 37 - Vector-Quantization using Self-Organizing Maps (SOM)