[mlpack] GSoC'18: Essential Deep Learning Modules

[Ewelina Nowak]

Hello


My name is Ewelina Nowak and I am 2nd-year student of Computer Science at
Gdansk University of Technology, Poland. I have experience in ML area, for
example: measuring heart-rate with EEG signals using several ML techniques
(publication), recognition and classification music mood in real-time
(thesis from my first field of study), drone detection using camera and
microphone arrays (projects done at my internships). I am currently at
internship at Intel Nervana which helps develop my skills and experience in
the AI area.

Recently, I have started to familiarize myself with mlpack code. I
downloaded mlpack, compiled it from source and set up a development
environment. Currently I am reading mlpack tutorials and I try to
experiment with some mlpack ML implementations to get better understanding
of the project.

I am interested in participating in GSoC 2018 and I am particularly
interested in Essential Deep Learning Modules. After reading proposed
papers and after doing some research I have three propositions for ANN
architectures in which I am interested:

1. RCNN (Recurrent Convolutional Neural Networks): I have an experience in
using RNN (with LSTM and GRU units) with CNN in one of my projects: Music
mood classification using deep learning modules. I used images from
spectral analysis for predicting mood of a song in time.

2. R-CNN (Regional based Convolutional Neural Networks): This architecture
can be used for object detection and classification. It can be used for
modern modifications of R-CNN: Fast R-CNN, Faster R-CNN or for example Mask
R-CNN.

3. BRNN (Bidirectional Recurrent Neural Networks): As I previously
mentioned I have experience in Recurrent Neural Networks and I would be
interested in implementing BRNN for one of my new projects in text analysis
area.

Could you please give me more information if any of proposed architectures
can be interesting and useful in mlpack? I would be very grateful for any
help and hints.


Best wishes,

Ewelina Nowak
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