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