Post-release documentation tweaks (#8)

* Add pypi, travis and license badges to README

* Fix typo

* Add roadmap

* Add note about example dependencies

README.md

@@ -1,6 +1,9 @@
 # IBM Analog Hardware Acceleration Kit
 
+![PyPI](https://img.shields.io/pypi/v/aihwkit)
 [![Documentation Status](https://readthedocs.org/projects/aihwkit/badge/?version=latest)](https://aihwkit.readthedocs.io/en/latest/?badge=latest)
+[![Build Status](https://travis-ci.com/IBM/aihwkit.svg?branch=master)](https://travis-ci.com/IBM/aihwkit)
+![PyPI - License](https://img.shields.io/pypi/l/aihwkit)
 
 ## Description
 
@@ -73,7 +76,10 @@ for epoch in range(10):
 ```
 
 You can find more examples in the [`examples/`] folder of the project, and
-more information about the library in the [documentation].
+more information about the library in the [documentation]. Please note that
+the examples have some additional dependencies - you can install them via
+`pip install -r requirements-examples.txt`.
+
 
 ## What is Analog AI?
 
@@ -132,7 +138,7 @@ compiling the sources, using a build system based on `cmake`.
 ## Authors
 
 IBM Analog Hardware Acceleration Kit has been developed by IBM Research,
-with with Malte Rasch, Tayfun Gokmen, Diego Moreda and Manuel Le Gallo-Bourdeau
+with Malte Rasch, Tayfun Gokmen, Diego Moreda and Manuel Le Gallo-Bourdeau
 as the initial core authors, along with many [contributors].
 
 You can contact us by opening a new issue in the repository, or alternatively

docs/source/index.rst

@@ -14,6 +14,7 @@ Welcome to IBM Analog Hardware Acceleration Kit's documentation!
    design
    developer_install
    development_conventions
+   roadmap
    changelog.md
    api_reference
 

docs/source/roadmap.rst

@@ -0,0 +1,30 @@
+Project roadmap
+===============
+
+You are one of the early users of the *IBM Analog Hardware Acceleration Kit*.
+The initial ``0.1.0`` release was focused on releasing a basic Pytorch
+integration for exploring selected features of the analog devices simulator,
+and set the basis that will be extended and improved upon:
+
+* integration of more simulator features in the PyTorch interface
+* tools to improve inference accuracy by converting pre-trained models
+  with hardware-aware training
+* algorithmic tools to improve training accuracy by compensating for material
+  short-comings
+* additional analog neural network layers
+* additional analog optimizers
+* custom network architectures and dataset/model zoos
+* integration with the cloud
+* hardware demonstrators
+
+This document will be updated with more details as the roadmap for the project
+evolves. As a companion, please refer to the `Issues`_ tab in the repository
+for more in-depth details about the status of the implementation of the
+different features and a sneak peek into the next release.
+
+We have an ambitious plan to incrementally bring new simulation and hardware
+features to our users, but we are eager to hear your feedback on the features
+of value for your work. Please contact us at ``[email protected]`` for any
+feedback or information.
+
+.. _Issues: https://github.com/IBM/aihwkit/issues