Summary of the Chapter#
CNNs are a powerful evolution of traditional neural networks tailored to work with spatial data like images. Their architecture—built on convolution, activation, and pooling layers—enables them to learn hierarchical, spatially-aware representations that general neural networks can’t.
A CNN consists of several key layers:
Convolutional layers: Extract features using learnable filters
Pooling layers: Reduce spatial dimensions (max pooling, average pooling)
Fully connected layers: Perform classification/Regression.
Activation functions: Introduce non-linearity (ReLU, softmax)
As we move forward, CNNs form the basis for advanced models in vision and even inspire architectures used in other domains such as NLP (e.g., CNN-based text classifiers).