MNIST
Projects with this topic
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Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)
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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.
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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.
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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"
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Hand Writing (Numbers & Letters) Classification using Tensorflow.
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