392221 Deep Learning (V) (WiSe 2018/2019)

The lecture introduces the basic concepts of deep learning, in particular different popular architectures including restricted Boltzmann machines, convolutional neural networks, stacked autoencoders (as concerns feedforward models), and long short term memory models and recursive networks (as concerns recurrent models). Different tricks for training are introduced including optimiziation schemes such as ADAM, and training modi such as drop-out, and training set enhancement. Popular applications such as for image recognition will be discussed. On a practical side,mostly TensorFlow will be used with Keras as API.

Teilnahmevoraussetzungen, notwendige Vorkenntnisse

Programmierkenntnisse (Python oder vergleichbar), Grundlagen Mathematik, Grundlagen maschinelles Lernen (etwa Neuronale Netze oder Einführung maschinelles Lernen)