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MNIST

Introduction to AI & Data Analysis: Classification of Iris-dataset with classic Perceptron Classification of MNIST-dataset with MLP and CNN

Machine Learning Basics - implementation of: loss functions (cross entropy loss, L1 loss, L2 loss, hinge loss), regularizations (L1 regularizer, L2 regularizer, early stopping), gradient check, optimizers, training a simple deep model.

A web app that classifies drawn digits with an image classification model trained on the MNIST dataset.

TensorFlow examples for Artificial Neural Networks course including: MLP with softmax output layer; MLP and CNN for MNIST dataset; CNN for CIFAR-10 dataset with data augmentation; LSTM with CNN layer for IMDB sentiment classification task.