Welcome to the fourth video in a series introducing neural networks! In this video we write our first neural network as a function. It takes random parameters (w1, w2, b) and measurements (m1, m2) and outputs predictions between 0 and 1. The predictions are random because the parameters are random.
In the next video we'll look at something called a cost function, and start looking into how calculus can help us minimize this function, which in turn gets us better weights for our neural network.
