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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js
| Date | Stars |
|---|---|
| 2026-07-24 | 1220 |
| 2026-07-25 | 1220 |
| 2026-07-28 | 1220 |
| 2026-07-30 | 1220 |
| 2026-08-06 | 1220 |
Today
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Momentum
0.0
growth rate 0.00%/day
<img src="https://cdn-images-1.medium.com/max/800/1*THG2__H9YHxYIt2sulzlTw.png" width="100%"/>
### Neataptic ([unmaintained](https://github.com/wagenaartje/neataptic/issues/112))
<a href="https://www.npmjs.com/package/neataptic"><img src="https://img.shields.io/npm/v/neataptic.svg?style=flat-square"></a>
<a href="https://www.npmjs.com/package/neataptic"><img src="https://img.shields.io/npm/dm/neataptic.svg?style=flat-square"></a>
<a href="https://travis-ci.org/wagenaartje/neataptic"><img src="https://img.shields.io/travis/wagenaartje/neataptic/master.svg?style=flat-square"></a> [](https://slack.bri.im)
<hr>
Neataptic offers flexible neural networks; neurons and synapses can be removed with a single line of code. No fixed architecture is required for neural networks to function at all. This flexibility allows networks to be shaped for your dataset through neuro-evolution, which is done using multiple threads.
```js
// this network learns the XOR gate (through neuro-evolution)
var network = new Network(2,1);
var trainingSet = [
{ input: [0,0], output: [0] },
{ input: [0,1], output: [1] },
{ input: [1,0], output: [1] },
{ input: [1,1], output: [0] }
];
await network.evolve(trainingSet, {
equal: true,
error: 0.03
});
```
Neataptic also backpropagates more than 5x faster than competitors. [Run the tests yourself](https://jsfiddle.net/tuet004f/11/). This is an example of regular training in Neataptic:
```js
// this network learns the XOR gate (through backpropagation)
var network = new architect.Perceptron(2, 4, 1);
// training set same as in above example
network.train(trainingSet, {
error: 0.01
});
network.activate([1,1]); // 0.9824...
```
Use any of the <b>6</b> built-in networks with customisable sizes to create a network:
```javascript
var myNetwork = new architect.LSTM(1, 10, 5, 1);
```
Or built your <b>own</b> network with pre-built layers:
```javascript
var input = new Layer.Dense(2);
var hidden1 = new Layer.LSTM(5);
var hidden2 = new Layer.GRU(3);
var output = new Layer.Dense(1);
input.connect(hidden1);
hidden1.connect(hidden2);
hidden2.connect(output);
var myNetwork = architect.Construct([input, hidden1, hidden2, output]);
```
You can even built your network neuron-by-neuron using <b>nodes</b> and <b>groups</b>!
<details>
<summary><a href="https://wagenaartje.github.io/neataptic/docs/">Visit the wiki to get started</a></summary>
</details>
<details>
<summary><a href="https://wagenaartje.github.io/neataptic/articles/playground/">or play around with neural networks</a></summary>
</details>
<img src="https://i.gyazo.com/27e8003df60dbbd21e240a53f8ec093a.png" width="33%"/><img src="https://i.gyazo.com/5325ca9217dbca3151a891739548a01d.png" width="33%"/><img src="https://i.gyazo.com/f566d2364af43dd3a78c8926ed204a51.png" width="33%"/>
## Examples
Neural networks can be used for nearly anything; driving a car, playing a game and even to predict words! At this moment,
the website only displays a small amount of examples. If you have an interesting project that you want to share with other users
of Neataptic, feel free to create a pull request!
<details>
<summary><a href="https://wagenaartje.github.io/neataptic/articles/neuroevolution/">Neuroevolution examples</a> (supervised)</summary>
</details>
<details>
<summary><a href="http://stackoverflow.com/questions/43574799/dead-simple-example-of-synaptic-js-lstm-rnn-algorithm/43734612#43734612">LSTM timeseries</a> (supervised)</summary>
</details>
<details>
<summary><a href="https://wagenaartje.github.io/neataptic/articles/classifycolors">Color classification</a> (supervised)</summary>
</details>
<details>
<summary><a href="https://wagenaartje.github.io/neataptic/articles/agario/">Agar.io-AI</a> (unsupervised)</summary>
</details>
<details>
<summary><a href="https://wagenaartje.github.io/neataptic/articles/targetseekingExcerpt of 5,475 characters
Read on GitHub
Thomas Wagenaar · Shield AI
451
96
13
6
Kanru Hua
5
5
4
3
3
2
2
1
1
1
1
1
1
Robin Wieruch · Consultant & Freelancer · Germany
1
1
Ori
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3b365b9149bfcc99, topic:neural-network