Backpropagation

Multi-Layer Perceptron (MLP) A Multi-Layer Perceptron (MLP) is one of the foundational types of artificial neural network. It learns to map inputs to outputs by passing data through a series of layers of interconnected nodes (“neurons”), adjusting internal weights during training until its predictions improve. Background: The Single Perceptron To understand an MLP, start with its building block — the perceptron (single neuron): It takes several numerical inputs $x_1, x_2, \ldots, x_n$. Each input is multiplied by a learned weight $w_i$ (how important that input is). The results are summed, a bias term $b$ is added (a constant that shifts the output), and the total is passed through an activation function $f$ to produce an output. $$\text{output} = f!\left(\sum_{i} w_i x_i + b\right)$$ ...