Implementing ANNs with TensorFlow
- High school math (matrix multiplications, derivations, vectors), further understanding of linear algebra is useful but not necessarily required
- basic programming experience in Python (+ NumPy is useful) or the willingness + time to learn it in the beginning of the course
This course serves as an Introduction to Deep Learning in both Theory and Implementation. While we discuss all major Neural Network architectures in use today we focus on Tensorflow/Keras for practising implementation.
At the end of the course, you should be able to design different kinds of ANNs on a theoretical basis to solve real-world problems and you should be able to implement and train the ANN with the help of TensorFlow.
The complete course material will be available online and no participation in fixed time slots is necessary. There will be multiple possibilities to work real-time together with tutors and lecturers for both questions on theory and coding. We believe this makes the course perfectly suited for students in not only Bachelor and Master programs in CogSci, but also for Students in Computational Cognition.
Ort: 69/117: Do. 12:00 - 14:00 (14x)
Do. 14:00 - 16:00 (14x),
93/E06: Fr. 10:00 - 14:00 (14x)
Zeiten: Do. 12:00 - 14:00 (wöchentlich) - Flipped classroom session, Ort: 69/117, Do. 14:00 - 16:00 (wöchentlich) - Coding support // occasional extension flipped Classroom session, Ort: 69/117, Fr. 10:00 - 14:00 (wöchentlich) - Coding Support Session, Ort: 93/E06
Erster Termin: Donnerstag, 21.10.2021 12:00 - 14:00, Ort: 69/117
Veranstaltungsart: Seminar (Offizielle Lehrveranstaltungen)
- Cognitive Science > Bachelor-Programm
- Cognitive Science > Master-Programm
- Data Science