## Background

Enhance the R package to interface with DeepLearning Frameworks, specifically Google's TensorFlow. DeepLearning is a broad subject and this work would focus on a subset of features that add value to the R community for example a scalable implementation of CovNets & LSTM.

## Related work

Currently there are no other packages. We are working on a package [deepLearnR](https://github.com/xsankar/deepLearnR) which implements initial features via rPython and skflow. The work on this GSOC proposal would be to enhance that package.

## Details of your coding project

R interfaces, datasets, vignettes and demos

## Expected impact

Interfaces to scalable deep learning frameworks is an essential capability to the R community. The bigger idea for the DeepLarnR package is to create a complete "wrapper" for TensorFlow probably starting with rPython eventually with rcpp as the c++ layer gets more richer interfaces

## Mentors

Krishna Sankar ([@](mailto:ksankar42 {at} gmail {dot} com))
Billy Vreeland ([@](mailto:billyvreeland {at} gmail {dot} com))

_Each project needs 2 mentors. Ideally one should be an expert R programmer with previous package development experience, and the other can be a domain expert in some other field or application area (optimization, bioinformatics, machine learning, data viz, etc)._

## Tests

### Easy
1. Install deepLearnR package and run all the examples. 
   * What are the results ?
   * What difficulties, if any, did you face installing the package ?
2. Change the data in the examples and show the results
   * You might have to tweak the learning rate and the epochs/steps
   * If you tweak the hyperparameters, show the results of the numbers you tried

### Medium
1. Write an R function that creates a machine learning model and returns the results
   * You can choose your favorite model and a dataset
   * The function should have hyperparameters (appropriate to your model) that can be tweaked
2. Write a model in python using TensorFlow or skflow
   * On your own, don't use the examples already on the net or part of tutrials

### Hard
1. Develop a small R package (say a new sumx function that adds two numbers and returns the result in a hex string)
2. Add all the required elements to pass R CMD check --as-cran <your package>

## Solutions of tests

1. Venali Sonone
  * https://github.com/venali/test