[mlpack] GSoC - 2017

[Marcus Edel]

Hello Prasanna,

Thanks for getting in touch.

> I have intermediate knowledge in deep learning field. I have also read Vivek's discussion on this topic and it gave me more information on the project idea. I have studied RBM, DBN and Hopfield network from book "Machine learning: an algorithmic perspective, Stephen Marsland". The book gave me idea of these networks and basic training algorithm for these models. I have implemented basic training algorithm but I am unaware of latest research going on in these models?

I've linked some recent papers on the ideas pages, that revisit some of the
traditional models like "Spike and slab restricted boltzmann machine" by
Courville et al or "Back to the Future: Radial Basis Function Networks
Revisited" by Que and Belkin paper. There are a couple more references for the
mentioned models, especially for GAN there are some really interesting papers
that are definitely worth a look.

> The project page says that this project will implement RBM, DBN, BRNN and GAN networks. Are there any other modules expected in this project? Also is there anything you can help me get started with this project? (any issues / bugs related to this part of code)


As stated in the project description, all models mentioned are just examples
that could be implemented, we don't expect you to implement all models or any of
the models. Meaning if you like to implement another network model that mlpack
currently doesn't have, we are open to discuss the ideas with you.

> As of now I am reading DeepLearningBook to dive into latest research going on in deep learning fields. It has variety of models / layers (for e.g. batch norm layer, GRU cell) which are not part of mlpack as of now. So I just wanted to know which methods are exactly expected from deep learning project? Also can I come up with my own project that implements these other methods which are not part of current project idea ?


If you like to implement machine learning algorithms that mlpack currently
doesn't have, contributions like that are more than welcome. If you can provide
a fast, well-tested implementation it would be great to include it in mlpack.
Also, we are open for own project ideas, however, we have to make sure it's
enough work and someone is able to mentor the project. The GRU layer and batch
norm is definitely an interesting idea that when extended by some other methods
could build up an interesting GSoC project.

> It would be much helpful if you can point me to some direction to get started with this project. Also it would be great if I can get my hands on issue related to ann module of mlpack.

I'll see if I can open some ann related issues, that could be a great start to
dive into the codebase.

Thanks,
Marcus


> On 10 Feb 2017, at 01:52, Prasanna Patil <prasannapatil08 at gmail.com> wrote:
> 
> Hello everyone,
> 
> I am new to mlpack. I am 3rd year computer engineering undergraduate student from India. I am going to participate in GSoC this year and I would like to work with mlpack on "Essential Deep Learning Modules" project.
> 
> 
> I have compiled mlpack locally and everything is set up. I have been exploring mlpack code base and I have created some basic ml programs using mlpack.
> 
> 
> I have completed coursera machine learning course and udacity deep learning course. I have implemented a few machine learning algorithm such as neural net, decision tree, hopfield network, self organizing map, k means etc. myself. However they are implemented in Python. You can check them here (https://github.com/prasanna08/machinelearning <https://github.com/prasanna08/machinelearning>).
> 
> 
> I have intermediate knowledge in deep learning field. I have also read Vivek's discussion on this topic and it gave me more information on the project idea. I have studied RBM, DBN and Hopfield network from book "Machine learning: an algorithmic perspective, Stephen Marsland". The book gave me idea of these networks and basic training algorithm for these models. I have implemented basic training algorithm but I am unaware of latest research going on in these models?
> 
> 
> The project page says that this project will implement RBM, DBN, BRNN and GAN networks. Are there any other modules expected in this project? Also is there anything you can help me get started with this project? (any issues / bugs related to this part of code)
> 
> 
> As of now I am reading DeepLearningBook to dive into latest research going on in deep learning fields. It has variety of models / layers (for e.g. batch norm layer, GRU cell) which are not part of mlpack as of now. So I just wanted to know which methods are exactly expected from deep learning project? Also can I come up with my own project that implements these other methods which are not part of current project idea ?
> 
> It would be much helpful if you can point me to some direction to get started with this project. Also it would be great if I can get my hands on issue related to ann module of mlpack.
> 
> Thanks,
> Prasanna
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