Answer / Kalpana

A Convolutional Neural Network (CNN) consists of several layers, each serving a specific purpose. Here are the main types of layers in a CNN:

1. Convolution Layer: This layer performs the actual convolution operation on the input data using filters or kernels. The output is called the convolved feature map.
2. Pooling Layer: Also known as downsampling layers, pooling layers reduce the spatial dimensions of the convolved feature maps, making the model computationally efficient and less prone to overfitting. Common pooling methods include max pooling, average pooling, and sum pooling.
3. Activation Layer: Each convolution and fully connected layer in a CNN has an activation function applied to its output values. The activation functions introduce non-linearity, allowing the network to learn complex patterns. Examples of activation functions are ReLU, sigmoid, and tanh.
4. Fully Connected Layer: Also known as dense or fully connected layers, these layers connect every neuron in one layer to every neuron in another layer. The last fully connected layer is typically the output layer and used for classification or regression tasks.
5. Flatten Layer: Before feeding data into a fully connected layer, the convolved feature maps are often flattened or reshaped into a single vector. This allows the network to perform operations in a lower dimensional space.

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