<ul><li>CNNs are powerful deep learning models designed to process visual data like images. They automate feature extraction and classification, making them essential in fields like medical imaging, autonomous driving, and facial recognition.</li><li>CNN architecture consists of Convolutional Layers and Pooling Layers. The Convolutional Layers apply filters to extract features and the Pooling Layers reduce the size of feature maps.</li><li>The size of the feature map may not always reduce in every Convolutional Layer. This is because of the padding, stride, and the input and filter dimensions.</li><li>Pooling techniques like Max Pooling, Average Pooling, Global Pooling, Adaptive Pooling, and Mixed Pooling are used in CNNs to further reduce the size of feature maps and improve memory efficiency.</li></ul>

Understanding Convolutional Neural Networks (CNNs) — A Simplified Overview

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