MNIST

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

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.

Test project for neural networks - Handwritten digit recognition on MNIST dataset

EMNIST and MNIST data sets

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.

DI assignment on encoding and classifying 28x28 images from the MNIST database. Includes a script for optimising the variables of the autoencoder using a natural evolution strategy algorithm.

Also known as "The Tradegy of Darth Python the Scripter"

Hand Writing (Numbers & Letters) Classification using Tensorflow.