How Convolution Neural Networks work —with Image Classification Keras Code

In this blog, we will be discussing a convolutional neural network with an example implementation on python. The convolutional neural network is also called as ConvNets or CNN, It is an artificial network so far mostly used on image analysis. Image analysis is where CNN’s are widely used but CNN can be used in other classification problems as well. Most generally we can think of CNN as an Artificial Neural Network that has some type of specialization to be able to pick out or detect patterns.

This pattern makes CNN so useful for image analysis. CNN is some form of an artificial neural network, so what differentiates it from a standard multi-layer perceptron(MLP). CNN has hidden layers called the convolutional layer, these layers make a CNN.

What is a Convolutional Layer?

A Convolutional layer just like any other layers a convolutional layer receives input then transforms input in some form and outputs the transformed input to the next layer with a convolutional layer this transformation is called convolutional operation. These layers play a major role in detecting patterns. In each convolutional layer, we need to specify the number of filters the layers should have.