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@@ -86,7 +86,7 @@ npm install brain.js
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### Installation note
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`Brain.js` depends on a native module `headless-gl` for gpu support. In most cases installing `brain.js` from npm should just work. However, if you run into problems, this mean prebuilt binaries are not able to download from github repositories and you might need to build it yourself.
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`Brain.js` depends on a native module [`headless-gl`](https://www.npmjs.com/package/headless-gl) for GPU support. In most cases installing `brain.js` from npm should just work. However, if you run into problems, this means prebuilt binaries are not able to download from GitHub repositories and you might need to build it yourself.
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#### Building from source
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###### Mac OS X
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-[Python 3.7 or later](https://www.python.org/)
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-[A supported version of Python](https://devguide.python.org/versions)
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-[XCode](https://developer.apple.com/xcode/)
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###### Ubuntu/Debian
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-[Python 3.7 or later](https://www.python.org/)
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-[A supported version of Python](https://devguide.python.org/versions)
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- A GNU C++ environment (available via the `build-essential` package on `apt`)
You can check out this fantastic screencast, which explains how to train a simple neural network using a realworld dataset: [How to create a neural network in the browser using Brain.js](https://scrimba.com/c/c36zkcb).
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You can check out this fantastic screencast, which explains how to train a simple neural network using a real-world dataset: [How to create a neural network in the browser using Brain.js](https://scrimba.com/c/c36zkcb).
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## Training
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The network will stop training whenever one of the two criteria is met: the training error has gone below the threshold (default `0.005`), or the max number of iterations (default `20000`) has been reached.
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By default training will not let you know how it's doing until the end, but set `log` to `true` to get periodic updates on the current training error of the network. The training error should decrease every time. The updates will be printed to console. If you set `log` to a function, this function will be called with the updates instead of printing to the console.
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By default, training will not let you know how it's doing until the end, but set `log` to `true` to get periodic updates on the current training error of the network. The training error should decrease every time. The updates will be printed to the console. If you set `log` to a function, this function will be called with the updates instead of printing to the console.
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However, if you want to use the values of the updates in your own output, the `callback` can be set to a function to do so instead.
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The learning rate is a parameter that influences how quickly the network trains. It's a number from `0` to `1`. If the learning rate is close to `0`, it will take longer to train. If the learning rate is closer to `1`, it will train faster, but training results may be constrained to a local minimum and perform badly on new data.(_Overfitting_) The default learning rate is `0.3`.
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