A CNN consists of several layers that process input data to make predictions. The main layers in a CNN are:

The Convolution Layer applies filters or kernels to extract important features from the input image. Each filter is a set of weights that highlight specific patterns or characteristics.

The Pooling Layer reduces the spatial dimensions of the feature maps generated by the Convolution Layer. It helps to retain the most important information while reducing the computational complexity.

The Flattening Layer transforms the pooled feature maps into a one-dimensional vector. This step is necessary to connect the output of the previous layers to the Fully Connected Layer.

The Fully Connected Layer, also known as the Dense Layer, makes the final decision or prediction based on the extracted features. It connects every neuron in the previous layer to every neuron in this layer, allowing for decision-making.

During the forward pass, the input data is passed through each layer from left to right, producing the final output. This process is known as the forward pass in a CNN.

For training the network, we need to pass the gradient of loss backward through each layer. This process is known as the backward pass or backpropagation. It allows the network to update the parameters of the CNN using Gradient Descent.