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tags | ||
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.vscode/launch.json | ||
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dataset/mnist.raw |
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# Modern ConvNetJS | ||
# InspireNN | ||
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It is a project trying to reimplement [ConvNetJS](https://github.com/karpathy/convnetjs) with modern styles (ECMAScript6, functional programming), making it's code shorter, more readable for beginners and easier to extend. | ||
This project makes neural networks running on browsers. It can be useful in blogging & presentation. | ||
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It reimplements [ConvNetJS](https://github.com/karpathy/convnetjs) with modern styles (ECMAScript6, functional programming), making it's code shorter, more readable for beginners and easier to extend. | ||
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It's sure that we should never expect a neural network training in the browser doing a big deal, but it's useful for presentation and understanding. | ||
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It is still under developing. | ||
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Following is a short introduction to ConvNetJS itself: | ||
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ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos. It currently supports: | ||
## Dependency | ||
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* transpiler | ||
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`systemjs` + `traceur` is used to load ES6 modules and provide ES6 polyfill for older browsers. | ||
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* style | ||
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`jquery`, `materializecss`, `d3js` and `three.js` is included in the project for supporting visualization tasks. Though making use of CDNs may be a better idea, but I prefer to keep them local for offline situations. | ||
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- Common **Neural Network modules** (fully connected layers, non-linearities) | ||
- Classification (SVM/Softmax) and Regression (L2) **cost functions** | ||
- Ability to specify and train **Convolutional Networks** that process images | ||
- An experimental **Reinforcement Learning** module, based on Deep Q Learning | ||
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For much more information, see the main page at [convnetjs.com](http://convnetjs.com) | ||
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## License | ||
MIT |
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[{"name":"mnist/","nodes":[{"name":"x","shape":[70000,28,28,1],"type":"images"},{"type":"Uint8Array","name":"y","length":70000}]}] |
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