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        "description": "# Time Series Neural Networks\n\n[![Build Status](https://travis-ci.org/sofienealouini/tsnn.svg?branch=master)](https://travis-ci.org/sofienealouini/tsnn) [![Coverage Status](https://coveralls.io/repos/github/sofienealouini/tsnn/badge.svg?branch=master&service=github)](https://coveralls.io/github/sofienealouini/tsnn?branch=master)\n\n\n\nTSNN is a deep learning library for time series forecasting built on Keras/Tensorflow. It implements various RNN-based models from recent research papers.\n\n## Getting Started\n\nThe following instructions will get you a copy of the project up and running on your local machine.\n\n### Prerequisites\n\nConda will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run TSNN.\n\n```\nconda create -n tsnn python=3.6\nsource activate tsnn\n```\n\n### Installing\n\nOnce you have activated your conda environment, you can easily install the package and all its dependencies from PyPI.\n\n```\npip install tsnn\n```\n\nA comprehensive tutorial on how to use TSNN is provided PackageTesting.ipynb notebook.\n\n\n## Built With\n\n* [Keras](http://www.dropwizard.io/1.0.2/docs/) - High level Deep Learning library running on top of Tensorflow / Theano / CNTK\n* [Tensorflow](https://maven.apache.org/) - Library for numerical computation, chosen as  Keras backend in TSNN.\n\n\n## Authors\n\n* **Sofiene Alouini** - *Engineering graduate - Machine Learning Enthusiast*\n\n\n\n\n",
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