[mlpack] Updated Example Zoo Ideas List

[Kaushal Choudhary]

There are three Working Zoo Examples I want to present in GSoc 2021 ,only
three because of the reduced time this year.

1. DCGAN(Deep Convolutional Generative Adversarial Networks) :- In this
project we will show an example of generating MNIST digits from a random
seed after training it with MNIST image Dataset . It consists of a
Generator(The Artist) and a Discriminator(The Art Critic) ,where the
generator tries to generate fake images of real ones and the discriminator
identifies the fake images .The discriminator is a CNN based image
classifier and generator uses conv2DTranspose for generating images. making
a GIF of this generator discriminator process will be a cool and effective
way to learn this model  .

2. Substitute for Example(2) :- Text Generation with RNN
In this example we will demonstrate how to generate text using character
base - RNN. We will use a dataset of shakespeare writings . Given a
sequence of characters from this data("shakespear") we will predict
the next character("e") , and longer sequences of data can generated after
calling the model repeatedly.
This will use embedding layers and GRU(type of RNN) and dense layers for
making the model.  For each character the model looks up the embedding runs
the GRU one timestep with embedding as input and applies the dense layer to
generate logits predicting the log likelihood of the next character.

3. Audio Clustering :- Audio dataset is yet to be used in mlpack ,well this
being a new dimension to mlpack library is quite interesting as well .
Students/Developers will be more intrigued and eager to understand . The
.WAV dataset is not supported in mlpack so for this I have to write a
custom WAV file reader and after applying STFT(short time fourier
transform) ,it will become an array and will be able to load in mlpack
using data::load and then convert it to a spectrogram for visualizing the
distribution . Then we can use clustering algorithms like spectral
clustering and ICA(independent component analysis) and other algorithms as
well .

Eager to hear from your side.

Thanks,

Kaushal
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