Convolutional Neural Networks (CNNs) - Explained

This video explains how Convolutional Neural Networks (CNNs) work for image recognition and computer vision, starting from a simple neural network and showing why images require a different architecture. It covers the key ideas behind convolution operations, kernels, feature maps, multi-channel inputs (RGB), pooling layers, and the full CNN pipeline used in deep learning. The video also explains the important inductive biases of CNNs, including local connectivity, translation equivariance, parameter sharing, translation invariance, and hierarchical feature learning, which make CNNs powerful fo…