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Neural Networks and Applications

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Lecture 13: Unconstrained Optimization: Gauss-Newtons Method

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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in

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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.

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