CNN Module on MNIST Dataset for Written Digit Classification

Abstract

Abstract: Convolutional Neural Networks (CNNs) have become indispensable tools in the realm of image classification, particularly in tasks like handwritten digit recognition. In this comprehensive study, we delve into the intricate world of CNN modules as applied to the MNIST dataset, a cornerstone benchmark in machine learning. Our research aims to meticulously assess the performance of diverse CNN architectures, encompassing variations in depth, convolutional layer configurations, pooling strategies, and regularization techniques. Through exhaustive experimentation and meticulous analysis, we endeavor to offer profound insights into the nuanced strengths and limitations of different CNN modules for the task of handwritten digit classification on the MNIST dataset. By elucidating the intricacies of CNN architecture, we endeavor to contribute to the advancement of image classification methodologies, particularly in domains where labeled data is scarce and precision is paramount.