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Unit I Introduction to ANN

Overview

This unit introduces the fundamental concepts of Artificial Neural Networks. It covers the historical development of neural networks, the structure and working mechanisms of biological neural networks, and the basic architecture of artificial neural networks. You'll explore the various topologies, features, and characteristics of neural networks, different types of neural networks, and the role of activation functions. Additionally, this unit delves into specific neuron models such as the McCulloch & Pitts model, the Perceptron, and the Adaline model. Basic learning laws and applications of neural networks are discussed, along with a comparison between biological and artificial neural networks.

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
  • ANN-Unit 1ANN-Unit 1Syllabus * Basics Concepts * Importance of tolerance of impression and uncertainty * Biological and artificial neuron * Single layer perceptron * Multi-layer perceptron * Supervised Learning * Unsupervised Learning * Back Propagation Network * Kohenen’s Self Organising Network * Hopefield Network Introduction to ANN * ANN is an efficient information processing system which resembles in characteristics with BNN * ANN posses large number of highly interconnected processing elements called nodes

Summary

  • High-level summary of the unit.

Questions

  • Key questions and discussion points.