Electronics - Neural Networks and Applications (English)

   Friday, February 14, 2025   

Starting a new Lecture Notes Series on Electronics - Neural Networks and Applications

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Electronics - Neural Networks and Applications By Lecture Notes together!

Lecture 1: Lec-1 Introduction to Artificial Neural Networks

Lecture 2: Lec-2 Artificial Neuron Model and Linear Regression

Lecture 3: Lec-3 Gradient Descent Algorithm

Lecture 4: Lec-4 Nonlinear Activation Units and Learning Mechanisms

Lecture 5: Lec-5 Learning Mechanisms-Hebbian,Competitive,Boltzmann

Lecture 6: Lec-6 Associative memory

Lecture 7: Lec-7 Associative Memory Model

Lecture 8: Lec-8 Condition for Perfect Recall in Associative Memory

Lecture 9: Lec-9 Statistical Aspects of Learning

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

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

Lecture 12: Lec-12 Single-Layer Perceptions

Lecture 13: Lec-13 Unconstrained Optimization: Gauss-Newtons Method

Lecture 14: Lec-14 Linear Least Squares Filters

Lecture 15: Lec-15 Least Mean Squares Algorithm

Lecture 16: Lec-16 Perceptron Convergence Theorem

Lecture 17: Lec-17 Bayes Classifier&Perceptron: An Analogy

Lecture 18: Lec-18 Bayes Classifier for Gaussian Distribution

Lecture 19: Lec-19 Back Propagation Algorithm

Lecture 20: Lec-20 Practical Consideration in Back Propagation Algorithm

Lecture 21: Lec-21 Solution of Non-Linearly Separable Problems Using MLP

Lecture 22: Lec-22 Heuristics For Back-Propagation

Lecture 23: Lec-23 Multi-Class Classification Using Multi-layered Perceptrons

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

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

Lecture 26: Lec-26 Radial Basis Function as ill-Posed Surface Reconstruc

Lecture 27: Lec-27 Solution of Regularization Equation: Greens Function

Lecture 28: Lec-28 Use of Greens Function in Regularization Networks

Lecture 29: Lec-29 Regularization Networks and Generalized RBF

Lecture 30: Lec-30 Comparison Between MLP and RBF

Lecture 31: Lec-31 Learning Mechanisms in RBF

Lecture 32: Lec-32 Introduction to Principal Components and Analysis

Lecture 33: Lec-33 Dimensionality reduction Using PCA

Lecture 34: Lec-34 Hebbian-Based Principal Component Analysis

Lecture 35: Lec-35 Introduction to Self Organizing Maps

Lecture 36: Lec-36 Cooperative and Adaptive Processes in SOM

Lecture 37: Lec-37 Vector-Quantization Using SOM