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README.md

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-# prototorch
-ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms. 
+# ProtoTorch
+
+ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
+prototype-based machine learning algorithms.
+
+![Tests](https://github.com/si-cim/prototorch/workflows/Tests/badge.svg?branch=master)
+
+## Description
+
+This is a Python toolbox brewed at the Mittweida University of Applied Sciences
+in Germany for bleeding-edge research in Learning Vector Quantization (LVQ)
+methods. Although, there are other (perhaps more extensive) LVQ toolboxes
+available out there, the focus of ProtoPy is ease-of-use, extensibility and
+speed.
+
+Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest
+Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized
+Matrix Learning Vector Quantization (GMLVQ) including the recent Learning Vector
+Quantization Multi-Layer Network (LVQMLN) are implemented using the "nn" API
+provided by PyTorch.
+
+## Installation
+
+ProtoTorch can be installed using `pip`.
+```
+pip install prototorch
+```
+
+## Usage
+
+ProtoTorch is modular. It is very easy to use the modular pieces provided by
+ProtoTorch, like the layers, losses, callbacks and metrics to build your own
+prototype-based(instance-based) models. These pieces blend-in seamlessly with
+numpy and PyTorch to allow you mix and match the modules from ProtoTorch with
+other PyTorch modules.
+
+ProtoTorch comes prepackaged with many popular LVQ algorithms in a convenient
+API, with more algorithms and techniques coming soon. If you would simply like
+to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
+lets you do this without requiring a black-belt in high-performance Tensor
+computation.