A neuron is a function that multiplies each input by a weight, adds them up with a bias, and puts the total through one bend. That is the whole thing. Everything a network can do comes from stacking that.
This is a bench rather than a course. Pick a set, build a network over it, train it, and then take it apart: every control stays live, so you can add and remove neurons, change the bend, break it on purpose, put one observation through by hand, and click any unit to see what it is doing.
The data
The network
Every hidden layer here is the identity, which applies no bend at all. A stack of linear maps is just one linear map, so however many neurons you add this network can only draw a straight line. That is the point of an activation function.
Training
00———————The loss
A loss is only a score against the same question, so a record is kept per set together with the three things that decide what the number means: whether the inputs were normalised, how much was held back, and the seed that chose which rows those were. Change any of them and the bench starts a new record rather than pretending the old one still applies. Nothing leaves this browser — there is no scoreboard behind this page, and clearing the site's data clears the book.
Neuron by neuron
One observation
Put a single row in and watch it go through. Each hidden value is what that unit answered for this row alone, and the last line is what the network made of them. Click any of them to pin that unit above.
Your own data
CSV, one row per example. Every column but the last is an input and must be a number; the last column is the answer. A numeric last column with many distinct values is treated as a regression, anything else as classes. Nothing is uploaded — the file is read in this tab.