Foundation of Artificial Neural Networks

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.