[mholt]

The chart at the right is the output/result of the neural network's training. In the foreground you see actual data points that are used to train the neural net: to "teach" it how to classify orange or blue (unless you choose "regression" in which case it computes a numeric value). In the background you see the gradient that is formed by the network. The goal is to make the gradient form around the data points by color as closely as possible.

The neural network is essentially the nodes in the middle, linked together by various weights. During training, the test data points are fed forward into the network, creating an output. That output is then fed backward using something called "back propagation" which is used to adjust the weights.

Typically, the more hidden layers or nodes per layer, the more difficult gradients that can be learned. Zero hidden layers essentially forms a linear gradient that can only be used to split very basic, linearly-separable data (drawing a straight line to separate the different types)

Neural networks have lots of little knobs and levers you can adjust. That's what all these inputs are that you see.