Lec-16 Perceptron Convergence Theorem

Course Lectures

• Lec-1 Introduction to Artificial Neural Networks

  Prof. Somnath Sengupta

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• Lec-2 Artificial Neuron Model and Linear Regression

  Prof. Somnath Sengupta

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• Lec-3 Gradient Descent Algorithm

  Prof. Somnath Sengupta

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• Lec-4 Nonlinear Activation Units and Learning Mechanisms

  Prof. Somnath Sengupta

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• Lec-5 Learning Mechanisms-Hebbian,Competitive,Boltzmann

  Prof. Somnath Sengupta

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• Lec-6 Associative memory

  Prof. Somnath Sengupta

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• Lec-7 Associative Memory Model

  Prof. Somnath Sengupta

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• Lec-8 Condition for Perfect Recall in Associative Memory

  Prof. Somnath Sengupta

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• Lec-9 Statistical Aspects of Learning

  Prof. Somnath Sengupta

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• Lec-10 V.C. Dimensions: Typical Examples

  Prof. Somnath Sengupta

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• Lec-11 Importance of V.C. Dimensions Structural Risk Minimization

  Prof. Somnath Sengupta

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• Lec-12 Single-Layer Perceptions

  Prof. Somnath Sengupta

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• Lec-13 Unconstrained Optimization: Gauss-Newtons Method

  Prof. Somnath Sengupta

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• Lec-14 Linear Least Squares Filters

  Prof. Somnath Sengupta

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• Lec-15 Least Mean Squares Algorithm

  Prof. Somnath Sengupta

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• Lec-16 Perceptron Convergence Theorem

  Prof. Somnath Sengupta

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• Lec-17 Bayes Classifier&Perceptron: An Analogy

  Prof. Somnath Sengupta

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• Lec-18 Bayes Classifier for Gaussian Distribution

  Prof. Somnath Sengupta

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• Lec-19 Back Propagation Algorithm

  Prof. Somnath Sengupta

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• Lec-20 Practical Consideration in Back Propagation Algorithm

  Prof. Somnath Sengupta

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• Lec-21 Solution of Non-Linearly Separable Problems Using MLP

  Prof. Somnath Sengupta

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• Lec-22 Heuristics For Back-Propagation

  Prof. Somnath Sengupta

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• Lec-23 Multi-Class Classification Using Multi-layered Perceptrons

  Prof. Somnath Sengupta

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• Lec-24 Radial Basis Function Networks: Cover's Theorem

  Prof. Somnath Sengupta

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• Lec-25 Radial Basis Function Networks: Separability&Interpolation

  Prof. Somnath Sengupta

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• Lec-26 Radial Basis Function as ill-Posed Surface Reconstruc

  Prof. Somnath Sengupta

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• Lec-27 Solution of Regularization Equation: Greens Function

  Prof. Somnath Sengupta

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• Lec-28 Use of Greens Function in Regularization Networks

  Prof. Somnath Sengupta

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• Lec-29 Regularization Networks and Generalized RBF

  Prof. Somnath Sengupta

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• Lec-30 Comparison Between MLP and RBF

  Prof. Somnath Sengupta

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• Lec-31 Learning Mechanisms in RBF

  Prof. Somnath Sengupta

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• Lec-32 Introduction to Principal Components and Analysis

  Prof. Somnath Sengupta

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• Lec-33 Dimensionality reduction Using PCA

  Prof. Somnath Sengupta

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• Lec-34 Hebbian-Based Principal Component Analysis

  Prof. Somnath Sengupta

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• Lec-35 Introduction to Self Organizing Maps

  Prof. Somnath Sengupta

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• Lec-36 Cooperative and Adaptive Processes in SOM

  Prof. Somnath Sengupta

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• Lec-37 Vector-Quantization Using SOM

  Prof. Somnath Sengupta

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