Deep Learning


  • Instructors Anselm Haselhoff, Fabian Küppers
  • Department: Computer Science

Machine learning vs. deep learning

Logistic regression and multilayer networks

Deep learning and optimization (e.g. weight initialization, regularization, data and batch Normalization, dropout, …)

Information Theory and Cost/Loss Function

Convolutional neural networks (e.g. onvolution and pooling, modern architectures) and object detection

Sequence modeling (e.g. long short­ term memory networks, memory augmented networks)

Embedding and representation learning (e.g. variational autoencoder, Word2Vec)