Predict on a breast cancer diagnosis using a neural network made from numpy

The past several blog posts that I have written about how to make a neural network from scratch. Making a neural network takes a bit of work, however, because each dataset is different and, as a consequence, each neural network is different too. The neural network that I will be discussing in this blog is a binary classifier neural network that takes in several features as input and produces one output.

A neural network is composed of an input layer, hidden layers, and an output layer:-

1. Input layer refers to the first layer of nodes in an artificial neural network. This layer receives input data from the outside world.
2. The hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. The hidden layers perform nonlinear transformations of the inputs entered into the network.
3. The output layer is the final layer in the neural network where desired predictions are obtained. There is one output layer in a neural network that produces the desired final prediction. It has its own set of weights and biases that are applied before the final output is derived.