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Tartarus Deep Learning Framework\n\n**Deep learning framework for TypeScript.** Run it on a browser, on AWS Lambda, or on anything that runs Node.js!\n\n\n[![Travis CI](https://travis-ci.org/salieri/tartarus-deep.svg?branch=master)](https://travis-ci.org/salieri/tartarus-deep/)\n[![Coverage Status](https://coveralls.io/repos/github/salieri/tartarus-deep/badge.svg?branch=master)](https://coveralls.io/github/salieri/tartarus-deep?branch=master)\n[![Codecov](https://codecov.io/gh/salieri/tartarus-deep/branch/master/graph/badge.svg)](https://codecov.io/gh/salieri/tartarus-deep)\n[![Codacy](https://api.codacy.com/project/badge/Grade/a7f08c24980f47e9b33a791903545fca)](https://www.codacy.com/app/salieri/tartarus-deep?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=salieri/tartarus-deep\u0026amp;utm_campaign=Badge_Grade)\n[![Maintainability](https://api.codeclimate.com/v1/badges/8ff95e28307f14562c3c/maintainability)](https://codeclimate.com/github/salieri/tartarus-deep/maintainability)\n[![David](https://david-dm.org/salieri/tartarus-deep.svg)](https://david-dm.org/salieri/tartarus-deep)\n[![David](https://david-dm.org/salieri/tartarus-deep/dev-status.svg)](https://david-dm.org/salieri/tartarus-deep?type=dev)\n\n\n## Features\n\nFrom-the-ground-up implementation for:\n\n*   **Math:** Vector and Matrix operations, seeded randomization\n*   **Graph:** Acyclic networks, automated data routing, support for multiple\n    input and output layers\n*   **Machine Learning:** Forward and back propagation, logistic regression,\n    gradient descent, loss (cost) functions, activation functions, optimizers,\n    metrics, dense layers, concat layers\n\n\n## Goals\n\n*   From-the-ground-up implementation for all standard deep learning operations\n*   Compatibility with Node.js, modern browsers, and AWS Lambda\n\n\n\n## Example\n\n```ts\nimport { Model, Dense, MemoryInputFeed } from '@tartarus/deep';\n\n/*\n * 1. Define a model \n *    - 4 input nodes\n *    - hidden layer with 5 nodes and sigmoid activation\n *    - output layer with 3 nodes and softmax activation \n */\nconst model = new Model({ optimizer: 'stochastic', loss: 'mean-squared-error' });\n\nmodel\n  .input(4)\n  .push(new Dense({ units: 5, activation: 'sigmoid' }))\n  .push(new Dense({ units: 3, activation: 'softmax' }));\n\nmodel.compile()\n  .then(\n    async () =\u003e {\n      /* 2. Prepare three samples of training data */\n      const feed = new MemoryInputFeed();\n      \n      feed\n        .add([1, 2, 3, 4], [1, 0, 0]) // .add(input, expected output)\n        .add([4, 3, 2, 1], [0, 1, 0])\n        .add([5, 6, 7, 8], [0, 0, 1]);\n      \n      \n      /* 3. Train model */\n      await model.fit(feed, { batchSize: 1, epochs: 100 });\n      \n      \n      /* 4. Predict */\n      const result = await model.predict([8, 9, 10, 11]);\n      \n      console.log(`Prediction: ${result.getDefaultValue().toJSON()}`);\n    }\n  );\n```\n\n## Model\n\n## Layers\n\n### Dense Layer\n\n### Concat Layer\n\n## Supplementary\n\n### Activation Functions\n\n### Loss Functions\n\n### Initializers\n\n### Metrics\n\n### Input\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsalieri%2Ftartarus-deep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsalieri%2Ftartarus-deep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsalieri%2Ftartarus-deep/lists"}