8.3324 Advances in deep learning (KOGW-MWPM-NIR)


Type Language Semester Credits Hours Room Time Term Year
S e 4 4 2 Do 14-16 S 2017
BSc: optional compulsory (Wahlpflichtbereich)
BSc examination field: Neuroinformatics (KOGW-WPM-NI)
MSc: Major subject
MSc major: Neuroinformatics and Robotics


Prerequisites: Basic math knowledge (MfA 1 or better), Basic python and TensorFlow knowledge, Knowledge of the basic concepts of deep neural nets, i.e. autoencoders, convolutional nets, LSTMs etc.

Recommended courses: Concepts and Applications of Neural Networks, Implementing ANNs with Tensor Flow

This seminar is an advanced course for students already familiar with the basics of deep neural nets, i.e., they are expected to know basic concepts such as auto encoders, convolutional nets, LSTMs, etc. Students will present a variety of state-of-the-art papers and concepts in the field of deep neural networks, and are given the opportunity to implement and adapt the corresponding architectures using TensorFlow. The course is limited to a maximum of 30 participants.

Link: http://www