This video introduces Convolutional Neural Networks (CNNs), focusing on image recognition. CNNs use convolutional layers with kernels to detect features (edges, shapes) and pooling layers to downsample, reducing computation. These layers are repeated to build abstraction, culminating in fully connected layers for classification. The video uses a number recognition example and an interactive resource (linked in description) for visualization. While simplifying some details (kernel types, hyperparameters), it explains the core process of feature extraction and classification within CNNs, contrasting them with feedforward networks. The video promotes supporting the channel and suggests further learning resources.