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.nowa...@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.e...@fu-berlin.de > <mailto:marcus.e...@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.nowa...@gmail.com >> <mailto:ewelina.anna.nowa...@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 >> >> >> _______________________________________________ >> mlpack mailing list >> mlpack@lists.mlpack.org <mailto:mlpack@lists.mlpack.org> >> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> >
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