Add contributing guide and improve docs (#28)

* Rename docs/models.md --> docs/index.md

* Add install instructions to the docs

* Add contributing guide

Closes #9

* Add note about adding models

* Address comments

CONTRIBUTING.md

@@ -0,0 +1,36 @@
+# Contributing to Larq
+
+👍 🎉 First off, thanks for taking the time to contribute! 👍 🎉
+
+**Working on your first Pull Request?** You can learn how from this _free_ series
+[How to Contribute to an Open Source Project on GitHub](https://egghead.io/courses/how-to-contribute-to-an-open-source-project-on-github).
+
+## Project setup
+
+To send a Pull Request it is required to fork Larq Zoo on GitHub.
+After that clone it to a desired directory:
+
+```shell
+git clone https://github.com/my-username/larq-zoo.git
+```
+
+Install all required dependencies for local development by running:
+
+```shell
+cd larq-zoo # go into the directory you just cloned
+pip install -e .[tensorflow] # Installs Tensorflow for CPU
+# pip install -e .[tensorflow_gpu] # Installs Tensorflow for GPU
+pip install -e .[test] # Installs all development dependencies
+```
+
+## Run Unit tests
+
+Inside the project directory run:
+
+```shell
+pytest .
+```
+
+## Code style
+
+We use [`black`](https://black.readthedocs.io/en/stable/) to format all of our code. We recommend installing it as a plugin for your favorite [code editor](https://black.readthedocs.io/en/stable/editor_integration.html).

docs/models.md → docs/index.md

@@ -3,6 +3,16 @@
 Larq Zoo provides reference implementations of deep neural networks with extremely low precision weights and activations that are made available alongside pre-trained weights.
 These models can be used for prediction, feature extraction, and fine-tuning.
 
+We believe that a collection of tested implementations with pretrained weights is greatly beneficial for the field of Extremely Quantized Neural Networks. To improve reproducibility we have implemented a few commonly used models found in the literature. If you have developed or reimplemented a Binarized or other Extremely Quantized Neural Network and want to share it with the community such that future papers can build on top of your work, please add it to Larq Zoo or get in touch with us if you need any help.
+
+## Installation
+
+Larq Zoo is not included in Larq by default. To start using it, you can install it with Python's [pip](https://pip.pypa.io/en/stable/) package manager:
+
+```shell
+pip install larq-zoo
+```
+
 Weights can be downloaded automatically when instantiating a model. They are stored at `~/.larq/models/`.
 
 ## Available models