In 1958, American psychologist and computer scientist Frank Rosenblatt published the influential paper "The Perceptron: a Probabilistic Model for Information Storage and Organization in the Brain" in the Cornell Psychological Review. The Perceptron, a fundamental building block of artificial neural networks, is a model inspired by the structure and function of biological neurons. It processes multiple inputs, assigns weights to each, and generates a single output usingin handling non-linearly separable data, they remain useful for basic image classification tasks, particularly on a threshold function. While single-layer perceptrons have limitations due to their inability to handle non-linearly separable data, they remain useful for basic image classification tasks, particularly with simpler datasets.

Perceptrons are versatile and can address a variety of problems, including classification, regression, and image recognition, where pixel values are fed as inputs, allowing the perceptron to learn how to classify images into distinct categories…