An MLP consists of at least three of nodes: an input layer, a hidden layer and an output layer. Except for the input node each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.
cd vision/mlp_mnist
julia --project mlp_mnist.jl