Merge pull request #6 from puneith/contributing

Contributing

CONTRIBUTING.md

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+# Contributing to Kubeflow Examples
+
+You want to contribute to Kubeflow examples? That's awesome! Please refer to the short guide below. 
+
+# Contributing Guide
+
+The [Kubeflow](https://github.com/kubeflow/kubeflow/blob/master/README.md) project is dedicated to making machine learning on Kubernetes simple, portable and scalable. We need your support in making
+this repo the destination for top models and examples, which show the power of Kubeflow. We have created an initial list of
+proposed examples. Please feel free to self-assign these examples, by following a simple 3 step process:
+
+* Identify an **example** in table below and put your github id against it
+* Create a Github issue with the details of the **example** and self-assign
+* Send a PR to this repo with the actual work for the example
+
+We have assigned priorities to the items below. See priority guidance: 
+
+* **P0**: Very important, try to self-assign if there is a P0 available
+* **P1**: Important, try to self-assign if there is no P0 available
+* **P2**: Nice to have
+
+# Proposed Examples
+
+| Example | What does it accomplish? | Priority | Priority reasoning | ML framework | Owner (github_id) | Company | Github issue |
+| -------- | :-----------------------: | :------: | :----------------: | :-----------: | :---------------: | :----: | :-----: |
+| TensorFlow serving end-to-end | How to perform TensorFlow serving on Kubeflow e2e | **P0** | TODO | TensorFlow | [nkash](https://github.com/nkashy1) | TODO | TODO |
+| [Zillow housing prediction](https://www.kaggle.com/c/zillow-prize-1/kernels) | Zillow's home value prediction on Kaggle | **P0** | High prize Kaggle competition w/ opportunity to show XGBoost | XGBoost | [puneith](https://github.com/puneith) | Google | [issue #16](https://github.com/kubeflow/examples/issues/16) |
+| [GitHub issue summarization](https://hackernoon.com/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8) | Text summarization on GitHub dataset | **P0** | End-to-end example of using Kubeflow for data science | scikit-learn | [texasmichelle](https://github.com/texasmichelle) | Google | [issue #14](https://github.com/kubeflow/examples/issues/14) |
+| [Mercari price suggestion challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge) | Automatically suggest product proces to online sellers | **P0** | | | | | |
+| [Airbnb new user bookings](https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings) | Where will a new guest book their first travel experience | | | | | | |
+| [TensorFlow object detection](https://github.com/tensorflow/models/tree/master/research/object_detection) | Object detection using TensorFlow API | | | | | | |
+| [TFGAN](https://github.com/tensorflow/models/blob/master/research/gan/tutorial.ipynb) | Define, Train and Evaluate GAN | GANs are of great interest currently | | | | | |
+| [Nested LSTM](https://github.com/hannw/nlstm) | TensorFlow implementation of nested LSTM cell | LSTM are the canonical implementation of RNN to solve vanishing gradient problem and widely used for Time Series | | | | | |
+| [How to solve 90% of NLP problems: A step by step guide on Medium](https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e) | Medium post on how to solve 90% of NLP problems from Emmanuel Ameisen | Solves a really common problem in a generic way. Great example for people who want to do NLP and don't know how to do 80% of stuff like tokenzation, basic transforms, stop word removal etc and are boilerplate across every NLP task | | | | | |
+| [Tour of top-10 algorithms for ML newbies](https://towardsdatascience.com/a-tour-of-the-top-10-algorithms-for-machine-learning-newbies-dde4edffae11) | Top 10 algorithms for ML newbies | Medium post with 8K claps and a guide for ML newbies to get started with ML | | | | | |