It has been a productive first week with
Mlpack, on my project
Implementing Essential Deep Learning Modules. The objective of the project is to implement the core infrastructure and API of some of the essential deep learning modules, primarily
Generative Adversarial Networks (GANs) and
Restricted Boltzmann Machines (RBMs), over the summers and maybe beyond!
For the upcoming Phase I evaluations, I'd be working almost exclusively on
GANs, which are one of the most reverred ideas in the field of Deep Learning today.
This week, and during the Community Bonding period, I worked on introducing the support for
Transposed Convolution and
Atrous Convolution layers, effectively completing the convolutional toolbox of
Mlpack, and the support for
Layer Normalization. I also discovered a couple of bugs in the existing code-base for
Batch Normalization and the
Naive Convolution rule, both of which have now been fixed. The pull requests are now merged and we are ready to begin on more implementation-heavy tasks such as the
I also opened an issue for the implementation of
.shed_cols() routines for
arma::Cube, which would help us in optimizing the calculation of gradients for
Atrous Convolutions. This is not a priority task for now and hence, would be taken up later.
For the coming week, I'll be spending most of my time away debugging the errors in Kris'
GAN implementation and hopefully, get it merged within the week itself.
Till Next Time Then!
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