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README.md

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 # TSNE-CUDA
+![GitHub release (latest by date)](https://img.shields.io/github/v/release/CannyLab/tsne-cuda)
+![Conda](https://img.shields.io/conda/pn/CannyLab/tsnecuda)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tsnecuda)
+![CUDA versions](https://img.shields.io/badge/cuda-10.1%20%7C%2010.2%20%7C%2011.0%20%7C%2011.1%20%7C%2011.2%20%7C%2011.3%20%20-blue)
+![Conda](https://img.shields.io/conda/dn/cannylab/tsnecuda)
+
 This repo is an optimized CUDA version of [FIt-SNE algorithm](https://github.com/KlugerLab/FIt-SNE) with associated python modules. We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. The paper describing our approach, as well as the results below, is available at [https://arxiv.org/abs/1807.11824](https://arxiv.org/abs/1807.11824).
 
 You can install binaries with anaconda for CUDA version 10.1 and 10.2 using `conda install tsnecuda -c cannylab -c pytorch`. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. [https://github.com/CannyLab/tsne-cuda/wiki/](https://github.com/CannyLab/tsne-cuda/wiki/)