Deep Learning, 5 cr

Ydinsisältö

• Deep neural networks layers: convolutional neural networks, recurrent neural networks, transformers, multilayer perceptrons
• Components of deep neural networks: nonlinearities, normalization, subsampling
• Task-specific loss functions
• Training deep neural networks: stochastic gradient descent, chain rule in gradient calculation, and flow of information
• DNN architectures: encoder-decoder structures, autoencoders, U-nets, handling the depth by residual and skip connections
• Supervised, self-supervised, adversarial learning
• Implementations in Python: Pytorch or Tensorflow