4. CNNs Learn Abstract Hierarchies, Not Just Shapes

Convolutional Neural Networks (CNNs) are explored in-depth as tools that extract spatial hierarchies from images. The book explains Conv2D, MaxPooling, Flatten, and dropout, while stressing the importance of data augmentation to fight overfitting. You learn how CNNs evolve from detecting edges to complex semantic patterns. Géron also covers how to visualize filters, interpret activations, and debug poor generalization. The emphasis is that architecture alone doesn’t yield accuracy — training protocols and data handling are just as crucial. CNNs aren’t magic; they’re structured pattern learners