[mlpack] GSoC'18: Essential Deep Learning Modules

[Marcus Edel]

Hello Ewelina,

welcome and thanks for getting in touch.

> 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.

Sounds like you already looked into some really interesting areas.

> 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.

Each idea is definitely interesting and would fit into the existing codebase, my
recommendation at this point is to focus on one or two ideas. (BRNN isn't enough
for the summer as a single project, but this would be a neat addition).

Let me know if I should clarify anything.

Thanks,
Marcus

> On 7. Mar 2018, at 16:53, Ewelina Nowak <ewelina.anna.nowak15 at gmail.com> wrote:
> 
> 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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