Convolutional neural networks(CNN) explanation and implementation part-1

Convolutional neural network (CNN) is a type of neural network architecture specially made to deal with visual data. In this article we will discuss the architecture of CNN and implement it on CIFAR-10 dataset in part-2. The main benefit of using a CNN over simple ANN on visual data is that CNN's are constrained to deal with image data exclusively. As we described above, a simple ConvNet is a sequence of layers, and every layer of a ConvNet transforms one volume of activations to another through a differentiable function. We use three main types of layers to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks).