Foundation Of Artificial Neural Networks

Basics of ANN, McCulloch Pitts Model, Perceptron, BackPropagation Model, Associative Network and Unsupervised model

What you'll learn

Understand the fundamentals of Artificial Neural Networks

Learn the various topologies and learning algorithms of ANN

Understand supervised learning network paradigms

Understand unsupervised learning network paradigms

Able to understand and solve problem for each neural network model

Requirements

No Pre-Requisite for this course. Students can listen to the lectures of the course Artificial Neural Network from base

Description

This course serves as an insightful exploration into the Basics of Artificial Neural Networks (ANN) and key models that have played pivotal roles in shaping the field of neural network research and applications. Covering foundational concepts from the McCulloch Pitts Model to advanced algorithms like Backpropagation, Associative Networks, and Unsupervised Models, participants will gain a comprehensive understanding of the principles driving modern artificial intelligence.Introduction to Artificial Neural Networks (ANN):Overview of Biological Neural Networks and inspiration behind developing Artificial Neural NetworksMcCulloch Pitts Model:In-depth examination of the McCulloch Pitts Model as a pioneering concept in neural network architecture. Understanding the basic principles that laid the groundwork for subsequent developments.Perceptron:Exploration of the Perceptron model as a fundamental building block of neural networks.Insight into how Perceptrons process information and make binary decisions.BackPropagation Model:Detailed study of the Backpropagation algorithm as a crucial element in training neural networks.Analysis of error backpropagation and its role in optimizing the performance of neural networks.Associative Network:Introduction to Associative Networks and the significance of connections between elements.Application of associative memory for pattern recognition and retrieval.Unsupervised Models:Comprehensive coverage of Unsupervised Learning in neural networks.Exploration of self-organizing maps, clustering, and other unsupervised techniques.This course is tailored for aspiring data scientists, machine learning enthusiasts, and professionals seeking to enhance their understanding of neural networks. Additionally, students and researchers interested in staying abreast of the latest developments in artificial intelligence will find this course invaluable. Embark on this educational journey to acquire a solid foundation in neural networks and gain the knowledge and skills necessary to navigate the dynamic landscape of artificial intelligence.

Overview

Section 1: Biological and Artificial Neuron

Lecture 1 Introduction to the course

Lecture 2 Introduction to ANN

Lecture 3 Characteristic of Brain

Lecture 4 Activation Functions, and Threshold

Section 2: McCulloch Pitts Neural Model

Lecture 5 McCulloch Pitts - Introduction

Lecture 6 McCulloch Pitts - Example 1

Lecture 7 McCulloch Pitts - Example 2

Section 3: Backpropagation network

Lecture 8 BackPropagation Network Introduction

Lecture 9 BackPropagation Network Working

Lecture 10 BackPropagation Network Activation Function and Working

Lecture 11 BackPropagation Network Example

Section 4: Associative Network

Lecture 12 Auto Associative Network

Lecture 13 Hetero Associative Network

Section 5: Unsupervised Learning

Lecture 14 Self Organizing Map

Computer science students,Beginners who want to learn Machine Learning,Students interested in understanding the basic working of Artificial Neural Network Models