The aim fo this project was to understand the effect of each hyperparameter in the classification accuracy of a model. Each subsequent model used the value of the best performing hyperparameter from the previous. Aiming to constantly create a better model with better classification accuracy.

• Analysis of the MNIST dataset
• Implementation of 5 DL models with FeedForward
• Accuracies of range 92-99%