Coding Convolutional Neural Networks (CNN) with TensorFlow

CNNs are one of the state of the art, Artificial Neural Network design architecture, with one of the best deep learning tools in areas such as image recognition and classification. The Basic Principle behind the working of CNN is the idea of Convolution, producing filtered Feature Maps stacked over each other. We'll be using MNIST dataset which is readily available in different libraries. Code has been written in a generic template so as to do very minimal modifications and can run on many datasets with very little change. Every CNN is made up of multiple layers, the three main types of layers are convolutional, pooling, and fully-connected.