ROBOT.LK

Video Tutorials on Neural Networks and Applications

1 - Introduction to Artificial Neural Networks
[media]http://www.youtube.com/watch?v=xbYgKoG4x2g[/media]

2 - Artificial Neuron Model and Linear Regression
[media]http://www.youtube.com/watch?v=KshIEHQn5ZM[/media]

3 - Gradient Descent Algorithm
[media]http://www.youtube.com/watch?v=KshIEHQn5ZM[/media]

4 - Nonlinear Activation Units and Learning Mechanisms
[media]http://www.youtube.com/watch?v=O4rU2pImSeo[/media]

5 - Learning Mechanisms- Hebbian,Competitive, Boltzmann
[media]http://www.youtube.com/watch?v=Zxs-f4HsTDk[/media]

6 - Associative memory
[media]http://www.youtube.com/watch?v=Ccrw9L1DK4g[/media]

7 - Associative Memory Model
[media]http://www.youtube.com/watch?v=TVKrhKYCu54[/media]

8 - Condition for Perfect Recall in Associative Memory
[media]http://www.youtube.com/watch?v=S_B9iBcq7OA[/media]

9 - Statistical Aspects of Learning
[media]http://www.youtube.com/watch?v=JC4uqegRLP0[/media]

10 - V.C. Dimensions: Typical Examples
[media]http://www.youtube.com/watch?v=vZjs6YutJAw[/media]

11 - Importance of V.C. Dimensions Structural Risk Minimization
[media]http://www.youtube.com/watch?v=aQa7w-8Q664[/media]

12 - Single-Layer Perceptions
[media]http://www.youtube.com/watch?v=VQ1O-pSPX20[/media]

13 - Unconstrained Optimization: Gauss-Newton's Method
[media]http://www.youtube.com/watch?v=22y9BPrgL_o[/media]

14 - Linear Least Squares Filters
[media]http://www.youtube.com/watch?v=7L6JqX6oJZQ[/media]

15 - Least Mean Squares Algorithm
[media]http://www.youtube.com/watch?v=wX7hy4Z9WAc[/media]

16 - Perceptron Convergence Theorem
[media]http://www.youtube.com/watch?v=tRG-OnnQ9g4[/media]

17 - Bayes Classifier & Perceptron: An Analogy
[media]http://www.youtube.com/watch?v=qtouJZBmqLk[/media]

18 - Bayes Classifier for Gaussian Distribution
[media]http://www.youtube.com/watch?v=tylvIqpk-bk[/media]

19 - Back Propagation Algorithm
[media]http://www.youtube.com/watch?v=nz3NYD73H6E[/media]

20 - Practical Consideration in Back Propagation Algorithm
[media]http://www.youtube.com/watch?v=Z19mdFFI2Z0[/media]

21 - Solution of Non-Linearly Separable Problems Using MLP
[media]http://www.youtube.com/watch?v=Fp33mf3L6AE[/media]

22 - Heuristics For Back-Propagation [58:05]
[media]http://www.youtube.com/watch?v=owzXIaZfjSM[/media]

23 - Multi-Class Classification Using Multi-layered Perceptrons
[media]http://www.youtube.com/watch?v=PN1-U6ZpO0E[/media]

24 - Radial Basis Function Networks: Cover's Theorem
[media]http://www.youtube.com/watch?v=yk_dsPu9Hmg[/media]

25 - Radial Basis Function Networks: Separability & Interpolation
[media]http://www.youtube.com/watch?v=NGo8Va_W--A[/media]

26 - Radial Basis Function as ill-Posed Surface Reconstruction
[media]http://www.youtube.com/watch?v=MluSDo19Y0w[/media]

27 - Solution of Regularization Equation: Greens Function
[media]http://www.youtube.com/watch?v=4U3P0LcaJcw[/media]

28 - Use of Greens Function in Regularization Networks
[media]http://www.youtube.com/watch?v=6n0uINcvx_E[/media]

29 - Regularization Networks and Generalized RBF
[media]http://www.youtube.com/watch?v=cE4ZWo3pcCk[/media]

30 - Comparison Between MLP and RBF
[media]http://www.youtube.com/watch?v=Q2gyX36LDyY[/media]

31 - Learning Mechanisms in RBF
[media]http://www.youtube.com/watch?v=-fWIiLZfcqE[/media]

32 - Introduction to Principal Components and Analysis
[media]http://www.youtube.com/watch?v=H0HjNuNvFVI[/media]

33 - Dimensionality reduction Using PCA
[media]http://www.youtube.com/watch?v=HnVYF6VQryU[/media]

34 - Hebbian-Based Principal Component Analysis
[media]http://www.youtube.com/watch?v=LBy50oz1coI[/media]

35 - Introduction to Self Organizing Maps
[media]http://www.youtube.com/watch?v=LjJeT7rwvF4[/media]

36 - Cooperative and Adaptive Processes in SOM
[media]http://www.youtube.com/watch?v=ftb0mwcl0Cg[/media]

37 - Vector-Quantization Using SOM
[media]http://www.youtube.com/watch?v=M4Zulu1MfU0[/media]