DeepLearningVsGaussianProcessClassification

In this project, we employ both Gaussian Processes and Deep Learning to solve a real-world time series multi-class classification problem. We used the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) dataset (https://www.kaggle.com/c/PLAsTiCC-2018). It's a huge dataset of about 37.37 GB containing the electromagnetic flux data measured from different celestial bodies in six different passbands. The task is to classify the type of astronomical bodies which include classes such as brown dwarfs, different types of supernovae, and galaxies.