[mlpack] GSOC - 2017 self-introduction

Marcus Edel marcus.edel at fu-berlin.de
Tue Mar 21 10:17:26 EDT 2017


Hello Arkadiy,

welcome and thanks for the introduction.

> I trained neural network (using Keras framework for Python) to play famous game
> Flappy Bird much better than a human can, that's why I am very interested in
> GSOC project connected with reinforcement learning. Also I participate in Kaggle
> competitions and coded my own small library in Python with most useful
> functions, such as cross-validation, parameters tuning and stacking models.

That sounds really interesting, is the code somewhere so that we could take a
look and probably play with it?

Also, the Reinforcement learning and cross-validation project has
been discussed at on the mailing list before:

http://mlpack.org/pipermail/mlpack/2017-March/003095.html
http://mlpack.org/pipermail/mlpack/2017-March/003098.html
http://mlpack.org/pipermail/mlpack/2017-February/003087.html

http://mlpack.org/pipermail/mlpack/2017-March/003212.html

Note that there are many more posts on this in the mailing list archive
to search for; those are only some places to get started.

I hope this is helpful,

Thanks,
Marcus

> On 21 Mar 2017, at 02:18, Arkadiy Dushatskiy <arcady27 at gmail.com> wrote:
> 
> Dear developers, I am very interested in participating in GSOC-2017 by working at one of your exciting projects.
>     I am a student from Moscow State University, Faculty of Computational Mathematics and Cybernetics, currently pursuing my 1st year of a Master’s degree in Applied Mathematics and Informatics. My main areas of interest are Artificial Intelligence and Machine Learning. The topic of my scientific research is GPU-accelerated neural networks. It is focused on image recognition, I investigate how the architecture of convolutional neural network and GPU characteristics affect the time consumption of forward propagation for a batch of images. The main technologies I use in scientific work are CUDA and cuDNN libraries (both for C++) for programming neural networks and Python for results visualization.
>     I have experience with reinforcement learning tasks, for instance, inspired by DeepMing articles, I trained neural network (using Keras framework for Python) to play famous game Flappy Bird much better than a human can, that's why I am very interested in GSOC project connected with reinforcement learning. Also I participate in Kaggle competitions and coded my own small library in Python with most useful functions, such as cross-validation, parameters tuning and stacking models. So, I am also interested in project about creating API for cross-validation and models tuning in mlpack.
> 
> Thank you for your attention!
> 
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