EMNIST-Classification: Using logistic regression in PyTorch to make a classification model on an in-built dataset, the EMNIST Dataset(letters).
EMNIST-Classification (1): Using a feed-forwarding neural network with 3 hidden layers in PyTorch to make a classification model on an in-built dataset, the EMNIST Dataset(letters).
EMNIST-Classification (2): Using a convolutional neural network with many chained layers in PyTorch to make a classification model on an in-built dataset, the EMNIST Dataset(letters).
EMNIST-Classification (3): Using a ResNet architecture with 2 residual blocks in PyTorch to make a classification model on an in-built dataset, the EMNIST Dataset(letters).
The final accuracy reached after employing various models, and selecting the best one(ResNet), is 95.33%.