In a nutshell
A multilayer perceptron is a stack of layers of neurons. Each neuron multiplies its inputs by learned weights, adds them up, and passes the total through a non-linear activation. Feeding numbers in at one end and reading the answer off the other is a forward pass. The hidden layers in the middle let the network learn its own features, so it can model curved, complex relationships a single straight line never could.