[mlpack] GSoC'18: Essential Deep Learning Modules

[Marcus Edel]

Hello Ewelina,

thanks for the feedback, sounds good and don't hesitate to ask if you have any
further question.

Thanks,
Marcus

> On 9. Mar 2018, at 23:58, Ewelina Nowak <ewelina.anna.nowak15 at gmail.com> wrote:
> 
> Hello Marcus, 
> 
> thank you for the suggestions! 
> 
> At this time both architectures (RCNN and R-CNN) seem interesting to me. For my first proposition - RCNN I have knowledge and experience in implementing this architecture, so it should be easier to prepare my implementation proposal. 
> 
> For the second architecture - R-CNN I think I need to read some articles to have better understanding. I will read [1], [2] and [3] for the weekend and I will start thinking about the implementation. 
> 
> I will let know after weekend if I have some questions and concerns. 
> 
> Thanks,
> Ewelina
> 
> ------------------------------------------------------------
> [1] https://arxiv.org/pdf/1311.2524v5.pdf  <https://arxiv.org/pdf/1311.2524v5.pdf>
> [2] https://arxiv.org/pdf/1504.08083.pdf  <https://arxiv.org/pdf/1504.08083.pdf>
> [3] https://arxiv.org/pdf/1506.01497v3.pdf  <https://arxiv.org/pdf/1506.01497v3.pdf>
> 
> 2018-03-07 21:56 GMT+01:00 Marcus Edel <marcus.edel at fu-berlin.de <mailto:marcus.edel at fu-berlin.de>>:
> 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 <mailto: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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