Deep Learning Lab
People
Rauber P.
Course director
Schlag I.
Assistant
Description
This course offers an opportunity to acquire practical experience in using TensorFlow to implement feedforward and recurrent neural networks (e.g., long short-term memory networks). Such networks currently achieve state-of-the-art results in many exciting tasks, such as object recognition, speech recognition, language translation, and learning to play games from experience. TensorFlow is an open source library developed by Google, and currently is perhaps the most popular choice among researchers and practitioners. Students will be evaluated through practical assignments.
RECOMMENDED COURSES
Machine Learning
REFERENCES
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Murphy, K. P. (2012).
- Machine learning: a probabilistic perspective. MIT Press. Graves, A. (2012).
- Supervised sequence labelling with recurrent neural networks. Springer. Nielsen, M. (2015).
- Neural networks and deep learning. Determination Press.
Education
- Master of Science in Artificial Intelligence, Core course, Lecture, 1st year
- PhD programme of the Faculty of Informatics, Elective course, 1st year (2 ECTS)
- PhD programme of the Faculty of Informatics, Elective course, 2nd year (2 ECTS)