The first neural network you want to build using squaring of numbers. Every time you want to learn about NNs or data science or AI, you search through google, you go through Reddit, get some GitHub codes. There is MNIST dataset, GANs, convolution layers, everywhere. Everybody is talking about neural networks. You pick up your laptop, run the code, Voila! it works.

There are three types of layer: input layer,hidden layer and output layer. Input layer is source of input data, we need normalize the data in the specific type or dimensions. Ok, this time, we implement a simplest neural network, it consist of one input layer, one hidden layer and one output layer. The previous blog, we use a linear formulation:y mx b, every variable is a number, this blog's demo, we will do matrix operation, if u unfamiliar with matrix, click this URL. Before we get start of our demo, let's see it in high level: The datasets contains of input data and output data, what we need to calculate is two weights.