From neurons to networks
Harshit Poddar
Abstract
Artificial Neural Networks (ANNs) have become versatile tools with applications in various domains. In this chapter, we explore the fascinating history of ANNs. We trace their origins from the inspiration of biological neurons to their transformation into powerful computational models. This chapter explores the principles, mechanisms, and use cases of ANNs. The text begins by discussing the shift from neurons as the foundation of artificial neural networks (ANNs) to the investigation of perceptrons and their single-layer structures. The focus shifts to applications in pattern recognition, classification, and decision-making, leading to the development of multi-layer perceptrons. The main goal is to provide readers with a thorough understanding of ANNs and perceptrons, regardless of their knowledge of AI. The chapter highlights the importance of knowledge acquisition by explaining various learning algorithms such as backpropagation, Hebbian and Kohonen mechanisms, and reinforcement learning. The chapter also highlights the importance of artificial neural networks (ANNs) in various domains such as computer vision, natural language processing, and more. The chapter ends by discussing the evolutionary journey of ANNs and emphasizing their limitless potential in solving complex problems. ANNs have progressed from imitating neurons to influencing AI progress, emphasizing their crucial role in our AI-driven future. This chapter provides readers with a deep insight into the impact of ANNs and paves the way for further exploration in various domains.