Description: Artificial Neural Networks (ANNs) are the foundation of major AI advancements, including natural language processing (chatbots, translation) and computer vision (self-driving cars, facial recognition). They provide a flexible, pattern-recognizing engine that allows AI to handle the complexity and nuance of real-world data, driving most significant AI breakthroughs today. As function approximators, ANNs excel at modeling intricate and often non-linear relationships within the data.

The basic ANN models map inputs to outputs, and are composed of many interconnected computational units. ANNs differ in their structure (layers, connections), function (data type handled), and complexity. This seminar will review the differing types of ANN architectures, learning paradigms, and activation functions.

CUI