Aim

To study the theoretical foundations of Convolutional Neural Networks (CNNs) and their application in image classification using color images. The experiment focuses on understanding convolution, feature maps, and pooling operations by implementing a small CNN on a selected subset of CIFAR-10. It further aims to analyze learned filters, intermediate feature maps, and class-wise performance to interpret how CNNs extract and classify visual features.