Neural Networks and Applications Video Lectures

Neural Networks and Applications
'Neural Networks and Applications' Video Lectures by Prof. Somnath Sengupta from IIT Kharagpur
"Neural Networks and Applications" - Video Lectures
1. Introduction to Artificial Neural Networks
2. Artificial Neuron Model and Linear Regression
3. Gradient Descent Algorithm
4. Nonlinear Activation Units and Learning Mechanisms
5. Learning Mechanisms-Hebbian,Competitive,Boltzmann
6. Associative memory
7. Associative Memory Model
8. Condition for Perfect Recall in Associative Memory
9. Statistical Aspects of Learning
10. V.C. Dimensions: Typical Examples
11. Importance of V.C. Dimensions Structural Risk Minimization
12. Single-Layer Perceptions
13. Unconstrained Optimization: Gauss-Newton's Method
14. Linear Least Squares Filters
15. Least Mean Squares Algorithm
16. Perceptron Convergence Theorem
17. Bayes Classifier & Perceptron: An Analogy
18. Bayes Classifier for Gaussian Distribution
19. Back Propagation Algorithm
20. Practical Consideration in Back Propagation Algorithm
21. Solution of Non-Linearly Separable Problems Using MLP
22. Heuristics For Back-Propagation
23. Multi-Class Classification Using Multi-layered Perceptrons
24. Radial Basis Function Networks: Cover's Theorem
25. Radial Basis Function Networks: Separability & Interpolation
26. Posed Surface Reconstruction
27. Solution of Regularization Equation: Greens Function
28. Use of Greens Function in Regularization Networks
29. Regularization Networks and Generalized RBF
30. Comparison Between MLP and RBF
31. Learning Mechanisms in RBF
32. Introduction to Principal Components and Analysis
33. Dimensionality reduction Using PCA
34. Hebbian-Based Principal Component Analysis
35. Introduction to Self Organizing Maps
36. Cooperative and Adaptive Processes in SOM
37. Vector-Quantization Using SOM
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