A typical CNN consists of several layers organized in a sequence, each of which serves a specific purpose. The key layers in a CNN include: Convolutional Layers: Extract features from the input data by applying convolution operations. Activation Layers: Apply a non-linear activation function (e.g., ReLU) to introduce non-linearity. Pooling Layers: Reduce the spatial dimensions of the data while retaining important information. Fully Connected (Dense) Layers: Process the extracted features and make predictions.