Vorlesung: Deep Learning for Visual Recognition


  • Dozent(en):
  • Beginn: 05.11.2020
  • Zeiten: Do. 10:15 - 11:45 (Online)
  • Veranstaltungsnummer: MA-INF 2313
  • Studiengang: Master
  • Aufwand: 6 CP
  • Prüfungen: 19th February, 16:00, and 29th March, 13:00


  • Betreuer:
  • Beginn: TBD
  • Zeiten: TBD


Neural networks are making a comeback!  Deep learning has taken over the machine learning community by storm, with success both in research and commercially.  Deep learning is applicable over a range of fields such as computer vision, speech recognition, natural language processing, robotics, etc.  This course will introduce the fundamentals of neural networks and then progress to state-of-the-art convolutional and recurrent neural networks as well as their use in applications for visual recognition.  Students will get a chance to learn how to implement and train their own network for visual recognition tasks such as object recognition, image segmentation and caption generation.

No formal pre-requisites.  Students should already be comfortable with concepts in probability theory and optimization and are recommended to have taken at least one course in machine learning or computer vision.  Exercises will be a mix of theory and practical (Python).

Weitere Dokumente



Übung 0: Setup
Übungsblatt  (PDF-Dokument, 67 KB)
Übung 1: MLBasics
Übungsblatt  (PDF-Dokument, 186 KB)
Übung 2: NeuralNetworks
Übungsblatt  (PDF-Dokument, 158 KB)
Übung 3: Optimization and Regularization
Übungsblatt  (PDF-Dokument, 161 KB)
Übung 4: Advanced Optimization and Initialization
Übungsblatt  (PDF-Dokument, 165 KB)
Übung 5: Normalization and Regularization
Übungsblatt  (PDF-Dokument, 195 KB)
Übung 6: CNNs
Übungsblatt  (PDF-Dokument, 146 KB)
Übung 7: ResNet
Übungsblatt  (PDF-Dokument, 168 KB)
Übung 8: AEs-GANs-RNNs (non-mandatory sheet)
Übungsblatt  (PDF-Dokument, 227 KB)