Total 37 Videos found in Category "Electronics - Neural Networks and Applications"


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

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