Predicting Uncertainty with Bayesian Neural Networks on MNIST Dataset
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Description
The objective of the project is class predictions with uncertainty with an implementation of a Bayesian Neural Network, trained and tested on the Rotated MNIST and Fashion MNIST datasets. The training process minimizes a loss function that considers both a Cross-Entropy loss and a Kullback-Leibler divergence loss term.