Unit II Learning Algorithms

Overview

This unit focuses on the algorithms that enable neural networks to learn and adapt. It starts with an introduction to the concepts of learning and memory, followed by a detailed examination of various learning algorithms. The unit covers the determination of the number of hidden nodes, error correction techniques, and gradient learning algorithms. Supervised learning is explored in depth, particularly the backpropagation algorithm used in multilayered network architectures. Additionally, the unit discusses feedforward and feedback neural networks, along with practical examples and applications of these learning algorithms.

Topics

• Introduction to ANNIntroduction to ANNDefinition Artificial Neural Networks (ANN) are computing systems inspired by the biological neural networks that constitute animal brains. Key Concepts Neurons**: Basic units of neural networks. Synapses**: Connections between neurons. Layers**: Input layer, hidden layers, output layer. Detailed Explanation * Explanation of what ANN is, how it functions, and its basic components. Diagrams * Links to Resources * Deep Learning by Ian Goodfellow * 3Blue1Brown Neural Networks Playlist Not
• History of Neural Networks
• Structure and Working of Biological Neural Networks
• Neural Net Architecture
• Topology of Neural Network Architecture
• Features and Characteristics
• Types of Neural Networks
• Activation Functions
• Models of Neurons: McCulloch & Pitts Model, Perceptron, Adaline Model
• Basic Learning Laws
• Applications of Neural Networks
• Comparison of Biological and Artificial Neural Networks (BNN and ANN)

Additional Resources

• Unit I: Introduction to ANN/Resources

Summary

• High-level summary of the unit.

Questions

• Key questions and discussion points.