Convolutional Neural Networks (CNNs) have revolutionized computer vision, becoming the standard for image classification and detection tasks. Unlike traditional neural networks, CNNs use convolutional layers to automatically detect spatial hierarchies of features, from simple edges to complex object shapes. In this article, we break down the architecture of a standard CNN, explaining the critical roles of pooling layers, stride, and padding in reducing dimensionality while preserving essential information. We also explore how transfer learning with models like MobileNetV2 can accelerate development time for modern applications.