Clean up file drop

* Restore README.md (delete README.txt)
* Delete dummy.cpp and add to .gitignore (this gets created every build)
* Remove stray .gitattributes

.gitignore

@@ -92,3 +92,5 @@ deps/env
 deps/local
 deps/build
 doc/
+
+/dummy.cpp

src/unity/python/README.md

@@ -0,0 +1,132 @@
+<img align="right" src="https://docs-assets.developer.apple.com/turicreate/turi-dog.svg" alt="Turi Create" width="100">
+
+# Turi Create 
+
+Turi Create simplifies the development of custom machine learning models. You
+don't have to be a machine learning expert to add recommendations, object
+detection, image classification, image similarity or activity classification to
+your app.
+
+* **Easy-to-use:** Focus on tasks instead of algorithms
+* **Visual:** Built-in, streaming visualizations to explore your data
+* **Flexible:** Supports text, images, audio, video and sensor data
+* **Fast and Scalable:** Work with large datasets on a single machine
+* **Ready To Deploy:** Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps
+
+Example: Image classifier with a few lines of code
+--------------------------------------------------
+
+If you want your app to recognize specific objects in images, you can build your own model with just a few lines of code:
+
+```python
+import turicreate as tc
+
+# Load data 
+data = tc.SFrame('photoLabel.sframe')
+
+# Create a model
+model = tc.image_classifier.create(data, target='photoLabel')
+
+# Make predictions
+predictions = model.predict(data)
+
+# Export to Core ML
+model.export_coreml('MyClassifier.mlmodel')
+```
+
+It's easy to use the resulting model in an [iOS application](https://developer.apple.com/documentation/vision/classifying_images_with_vision_and_core_ml):
+
+<p align="center"><img src="https://docs-assets.developer.apple.com/published/a2c37bce1f/689f61a6-1087-4112-99d9-bbfb326e3138.png" alt="Turi Create" width="600"></p>
+
+With Turi Create, you can can tackle a number of common scenarios:
+* [Recommender systems](https://apple.github.io/turicreate/docs/userguide/recommender/introduction.html)
+* [Image classification](https://apple.github.io/turicreate/docs/userguide/image_classifier/introduction.html)
+* [Image similarity](https://apple.github.io/turicreate/docs/userguide/image_similarity/introduction.html)
+* [Object detection](https://apple.github.io/turicreate/docs/userguide/object_detection/introduction.html)
+* [Activity classifier](https://apple.github.io/turicreate/docs/userguide/activity_classifier/introduction.html)
+* [Text classifier](https://apple.github.io/turicreate/docs/userguide/text_classifier/introduction.html)
+
+You can also work with essential machine learning models, organized into algorithm-based toolkits:
+* [Classifiers](https://apple.github.io/turicreate/docs/userguide/supervised-learning/classifier.html)
+* [Regression](https://apple.github.io/turicreate/docs/userguide/supervised-learning/regression.html)
+* [Graph analytics](https://apple.github.io/turicreate/docs/userguide/graph_analytics/intro.html)
+* [Clustering](https://apple.github.io/turicreate/docs/userguide/clustering/intro.html)
+* [Nearest Neighbors](https://apple.github.io/turicreate/docs/userguide/nearest_neighbors/nearest_neighbors.html)
+* [Topic models](https://apple.github.io/turicreate/docs/userguide/text/intro.html)
+
+System Requirements
+-------------------
+
+* Python 2.7 (Python 3.5+ support coming soon)
+* x86\_64 architecture
+* macOS 10.11+, Linux with glibc 2.12+ (including WSL on Windows 10)
+
+Installation
+------------
+
+For detailed instructions for different varieties of Linux see [LINUX\_INSTALL.md](https://github.com/apple/turicreate/LINUX_INSTALL.md).
+For common installation issues see [INSTALL\_ISSUES.md](https://github.com/apple/turicreate/INSTALL_ISSUES.md).
+
+We recommend using virtualenv to use, install, or build Turi Create. 
+Be sure to install virtualenv using your system pip.
+
+```shell
+pip install virtualenv
+```
+
+The method for installing *Turi Create* follows the
+[standard python package installation steps](https://packaging.python.org/installing/).
+To create a Python virtual environment called `venv` follow these steps:
+
+```shell
+# Create a Python virtual environment
+cd ~
+virtualenv venv
+```
+
+To activate your new virtual environment and install `Turi Create` in this environment, follow these steps:
+```shell
+# Active your virtual environment
+source ~/venv/bin/activate
+
+# Install Turi Create in the new virtual environment, pythonenv
+(venv) pip install -U turicreate
+```
+
+Documentation
+-------------
+
+The package [User Guide](https://apple.github.io/turicreate/docs/userguide) and [API Docs](https://apple.github.io/turicreate/docs/api) contain
+more details on how to use Turi Create.
+
+GPU Support
+-----------
+
+By default, `turicreate` takes a dependency on the default installation of
+`mxnet`. To enable GPU support after installation of the `turicreate` package,
+please perform the following steps:
+
+ * Install CUDA 8.0 ([instructions](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/))
+ * Install cuDNN 5 for CUDA 8.0 ([instructions](https://developer.nvidia.com/cudnn))
+
+Make sure to add the CUDA library path to your `LD_LIBRARY_PATH` environment
+variable.  In the typical case, this means adding the following line to your
+`~/.bashrc` file:
+
+```shell
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+```
+
+If you installed the cuDNN files into a separate directory, make sure to
+separately add it as well. Next step is to uninstall `mxnet` and install the
+CUDA-enabled `mxnet-cu80` package:
+
+```
+(pythonenv) pip uninstall -y mxnet
+(pythonenv) pip install mxnet-cu80==0.11.0
+```
+
+Make sure you install the same version of MXNet as the one `turicreate` depends
+on (currently `0.11.0`). If you have trouble setting up the GPU, the [MXNet
+installation instructions](https://mxnet.incubator.apache.org/get_started/install.html) may
+offer additional help.