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Deep Learning Lab

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 the most popular choice among researchers and practitioners. Students will be evaluated through practical assignments.

 

RECOMMENDED COURSES
Machine Learning

 

REFERENCES

  • Nielsen, M. (2015). Neural networks and deep learning. Determination Press
  • 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

People

 

Rauber P.

Course director

Schlag I.

Assistant

Stanic A.

Assistant

Additional information

Semester
Fall
Academic year
2019-2020
ECTS
3
Education
Master of Science in Artificial Intelligence, Core course, Lecture, 1st year

Master of Science in Computational Science, Elective course, Lecture, 2nd year

PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (2 ECTS)

PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (2 ECTS)