Lehrinhalte
Introduction to artificial intelligence, artificial neural networks, linear classifier, the perceptron, fully connected neural networks, training by backpropagation, loss and activation functions, weight initialization and regularization, optimizers, hyperparameter tuning, multi-task learning, convolutional neural networks (CNN), common network architectures and custom models, Deep Learning on 3D data and aerial laser scanning point clouds, time series geographical data, recurrent neural networks (RNN), attention mechanism, generative learning with Autoencoders and generative adversarial networks (GAN), Deep Learning libraries (e.g. TensorFlow, PyTorch).
