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I recently went through a very detailed and well-explained Python-based project/lesson by Karpathy which is called micrograd. This is a tiny scalar-valued autograd engine and a neural net on top of it.

The project above is, as expected, built on Python. For learning purposes, I wanted to see how such a network may be implemented in TypeScript and came up with a 🤖 micrograd-ts - https://github.com/trekhleb/... repository (and also with a demo - https://trekhleb.dev/micrograd-ts/ of how the network may be trained).

Trying to build anything on your own very often gives you a much better understanding of a topic. So, this was a good exercise, especially taking into account that the whole code is just ~200 lines of TS code with no external dependencies.

The micrograd-ts repository might be useful for those who want to get a basic understanding of how neural networks work, using a TypeScript environment for experimentation.

With that being said, let me give you some more information about the project.

## Project structure

- [micrograd/](https://github.com/trekhleb/...) — this folder is the core/purpose of the repo
- [engine.ts](https://github.com/trekhleb/...) — the scalar `Value` class that supports basic math operations like `add`, `sub`, `div`, `mul`, `pow`, `exp`, `tanh` and has a `backward()` method that calculates a derivative of the expression, which is required for back-propagation flow.
- [nn.ts](https://github.com/trekhleb/...) — the `Neuron`, `Layer`, and `MLP` (multi-layer perceptron) classes that implement a neural network on top of the differentiable scalar `Values`.
- [demo/](https://github.com/trekhleb/...) - demo React application to experiment with the micrograd code
- [src/demos/](https://github.com/trekhleb/...) - several playgrounds where you can experiment with the `Neuron`, `Layer`, and `MLP` classes.

Demo (online)
---------------------

To see the online demo/playground, check the following link:

🔗 https://trekhleb.dev/micrograd-ts