[mlpack] GSOC2020 ProPosal: Implementing Essential Deep Learning Modules(Marcus)

[Geeky Pathak]

Hello everyone,
I am Himansu Pathak final year undergraduate student. So, this summer I
want to work on  3 things to add  mlpack

1. First one Is trying to RBF as a layer in mlpack I am right now I am
working on this one. So, in this Implementation, I am trying to add a layer
for Radial Basis Function which can be used with a feed-forward network and
help us to construct a Radial Basis Function Networks.
To test this network I will try to use the same tests which are being used
to test vanilla network with dropout layer.

2. Second thing is to add RBF kernel right now we have only linear kernel
SVM in mlpack I want to add support for RBF kernel as well. So, to do this
I have two ideas in my mind
a) first one to add RBF kernel inside linear_svm directory and change its
name to SVM  just like we are doing with GANS
b) the second one will be to implement it in a separate place and adding
everything separately
Please suggest What do you think? for this one.

3 Last but not least is to implement a DBN (Deep Belief Nets). We already
have an implementation for RBM(Restricted Boltzmann Machine) in mlpack. So,
my task to implement this is quite simple I will try to change that
implementation So that I can use it as a layer. I need to implement some
visitor for this and create an implementation for DBN to train these (RBM)
layers.

It is a very short explanation of these problems I will try to elaborate on
them in my proposal.
Please suggest if you want any changes in any of these Ideas.
Regards,
Himanshu Pathak
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