// the find
trekhleb/nano-neuron
🤖 NanoNeuron is 7 simple JavaScript functions that will give you a feeling of how machines can actually "learn"
NanoNeuron implements gradient descent for a single linear neuron in ~100 lines of plain JavaScript, teaching the model to learn the Celsius-to-Fahrenheit formula from scratch. It is aimed at developers who want to understand what actually happens inside a training loop without fighting a framework. The README is the product — the code is a companion.
The walkthrough traces forward propagation, cost calculation, and backprop in the exact order a learner needs to see them, with no abstraction hiding the math. Every function is standalone and named after what it does, so you can read the code without cross-referencing a class hierarchy. The README shows the cost curve converging from ~4694 to ~0.000002, which makes the learning dynamic concrete rather than theoretical. Zero dependencies means anyone can paste the file into a browser console and run it immediately.
It only works for problems that are linearly separable by design — the README acknowledges this but it means you cannot extend this code to anything real without a full rewrite. The backward propagation derivation is glossed over; the formulas appear without derivation and the reader is pointed to an external site, which is the hardest part to actually understand. The training loop mutates a global `nanoNeuron` variable inside `trainModel`, a leaky closure that would confuse anyone trying to adapt the pattern. At 2.2k stars it is clearly useful as a reference, but it has not been touched substantively since it was published — there is no test file and no worked example of what breaks when you change alpha.