Hello! In this lesson, we'll thoroughly examine the inner workings of the crucial backpropagation algorithm in training neural networks and create it from scratch in Python.
A neural network consists of an input layer, one or more hidden layers, and an output layer. Each layer houses neurons, or nodes interconnected through links attributed with weights. These weights and bias terms dictate the network's output. In our Python code, the size of the input layer adjusts according to the shape of self.input. The hidden layer hosts four neurons (self.weights1), and the output layer accommodates one neuron (self.weights2).
Our activation function, the sigmoid function, transforms real-value numbers into a range between 0 and 1. Let's recall its mathematical definition:
