[Mahn]

Or perhaps explain how all the different inputs influence the result? I more or less get that it's just iterating over the data to approximate the given data set when you press play but I have no idea how giving it more or less neurons changes that, to name an example.

[shoemai]

Basically each input gets multiplied by some weight that gets adjusted through each iteration. The product of the input and weight gets put through an activation function, and the outcome of that can be interpreted as the network's prediction of the class.

So you see the first neuron's input is just x1. You can see in the little graph at x1 that it's split down the middle with orange on one side and blue on the other. You can think of adjusting the weight on that neuron as adjusting where along the x axis the split occurs. All points on the orange side are classified orange and all on the blue side are classified blue. If you picked a data set like the spiral one or whatever, that neuron alone isn't going to make very many correct classifications. That's because it only gets the x1 value as input and can only affect the output by multiplying x1 by some weight, which would only have the affect of shifting the classification boundary left or right. You can see the same thing happening for the second neuron with input x2 except that now it splits along the y axis. Again that alone isn't going to match the data very well.

But then you get to the second layer, and the input of each neuron in the second layer is the output of each neuron in the first layer. So these neurons are able to take into consideration both x1 and x2 and are able to divide the data in more complex ways. So you can think of the neurons in each layer of the neural network as being able to consider more and more complex properties of the data in forming its output.

[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.