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Deep Learning Lab

Descrizione

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

Persone

 

Rauber P.

Docente titolare del corso

Schlag I.

Assistente

Stanic A.

Assistente

Informazioni aggiuntive

Semestre
Autunnale
Anno accademico
2019-2020
ECTS
3
Offerta formativa
Master of Science in Artificial Intelligence, Corso di base, Corso, 1° anno

Master of Science in Computational Science, Corso a scelta, Corso, 2° anno

Dottorato in Scienze informatiche, Corso a scelta, Corso, 1° anno (2 ECTS)

Dottorato in Scienze informatiche, Corso a scelta, Corso, 2° anno (2 ECTS)