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Padhai By Astra Cloud

Elective Neural Networks

Neural Networks notes

6 chapter notes, in syllabus order. Each starts with the key points.

Unit 1 · 4 hrs

Neural Networks: Basics, Models & Biological InspirationUnit 1 of Neural Networks introduces the foundational concepts of artificial neural networks (ANNs), their biological inspiration, key components (neurons, layers, weights), and simple models like the McCulloch-Pitts neuron. It contrasts ANNs with traditional computing, explains activation functions, and demonstrates a9 min read

Unit 2 · 3 hrs

Rosenblatt’s Perceptron: Binary Classification, Learning Rules & LimitationsUnit 2 of Neural Networks covers the foundational Perceptron algorithm, its mathematical formulation, training via the Perceptron Learning Rule (PLR), geometric interpretation as a linear classifier, and its limitations (e.g., XOR problem). Includes real-world applications, worked examples, and comparisons with modern 10 min read

Unit 3 · 5 hrs

Regression in Neural Networks: Linear, Logistic & Model BuildingUnit 3 of Neural Networks explores regression techniques—linear, logistic, and polynomial—as foundational tools for model building in neural networks, covering mathematical formulations, real-world applications, and implementation steps with visual traces.9 min read

Unit 4 · 5 hrs

The Least-Mean-Square Algorithm · note coming

Unit 5 · 8 hrs

Multilayer Perceptron · note coming

Unit 6 · 7 hrs

Kernel Methods & RBF Networks: Kernels, RBF, SVM, and ApproximationUnit 6 of Neural Networks explores kernel methods (linear vs. non-linear transformations), Radial-Basis Function (RBF) networks (structure, training, and applications), and their comparison with Support Vector Machines (SVM). It covers mathematical foundations, real-world use cases, and implementation insights for TU/P12 min read

Unit 7 · 6 hrs

Self-Organizing Maps: Clustering, Topology, and ApplicationsUnit 7 of Neural Networks explores Self-Organizing Maps (SOMs), a type of unsupervised neural network used for dimensionality reduction, clustering, and visualization of high-dimensional data. This note covers their architecture, training algorithms, applications in pattern recognition, and comparisons with other clust12 min read

Unit 8 · 7 hrs

Dynamic Driven Recurrent Networks: RNNs, LSTMs, GRUsUnit 8 of Neural Networks explores Dynamic Driven Recurrent Networks (RNNs), covering their architecture, variants (LSTM, GRU), training challenges, and real-world applications in sequential data processing like time-series forecasting, natural language, and speech recognition.10 min read