What is CNN?

This is the simple application of a filter to an input that results in
inactivation. Repeated application of the same filter to input results in a
map of activations called a feature map, indicating the locations and
strength of a detected feature in input, such as an image.

Convolutional layers are the major building blocks which are used in
convolutional neural networks.

A covnets is the sequence of layers, and every layer transforms one
volume to another through differentiable functions.
Different types of layers in CNN:

Let’s take an example by running a covnets on of image of dimensions 32 x
32 x 3.
1. Input Layer: It holds the raw input of image with width 32, height 32
and depth 3.

2. Convolution Layer: It computes the output volume by computing dot
products between all filters and image patches. Suppose we use a
total of 12 filters for this layer we’ll get an output volume of dimension 32x 32 x 12.

3. Activation Function Layer: This layer will apply the element-wise
activation function to the output of the convolution layer. Some
activation functions are RELU: max(0, x), Sigmoid: 1/(1+e^-x), Tanh,
Leaky RELU, etc. So the volume remains unchanged. Hence output
volume will have dimensions 32 x 32 x 12.

4. Pool Layer: This layer is periodically inserted within the covnets, and
its main function is to reduce the size of volume which makes the computation fast reduces memory, and also prevents overfitting. Two
common types of pooling layers are max pooling and average
pooling. If we use a max pool with 2 x 2 filters and stride 2, the
resultant volume will be of dimension 16x16x12.

5. Fully-Connected Layer: This layer is a regular neural network layer
that takes input from the previous layer and computes the class
scores and outputs the 1-D array of size equal to the number of
classes.