Neural Networks: Feedforward and Backpropagation Explained

Neural networks consists of neurons, connections between these neurons called weights and some biases connected to each neuron. We distinguish between input, hidden and output layers, where we hope each layer helps us towards solving our problem. To move forward through the network, called a forward pass, we iteratively use a formula to calculate each neuron in the next layer. Keep a total disregard for the notation here, but we call neurons for activations $a$, weights w and biases b-- which is cumulated in vectors. This takes us forward, until we get an output.