Applied Deep Learning

Type of instruction




Part of degree program


Recommended in

Semester 3

Typically offered in

Autumn semester

Course description

In this course practical problems are addressed with deep learning techniques. Architectures: auto-encoders, convolutional neural networks, recurrent neural networks, long short-term memory, residual networks, and highway networks. Image processing: image restoration and super-resolution, bounding boxes, objects, face, hand, body recognition. Speech processing: speaker identification, speaker de-identification, speech recognition and speech production. Motion and control: deep learning for motion via imitation, dynamic movement primitives. Deep methods for anomaly detection, optical flow, tracking, multi-modal tracking, information fusion and pattern completion.


Compulsory readings:

  • Ian Goodfellow and Yoshua Bengio and Aaron Courville: Deep Learning. MIT Press, 2016. Hardcover: ISBN: 9780262035613, eBook: ISBN: 9780262337434

Recommended readings:

  • Jason Brownlee: Applied Deep Learning in Python Mini-Course
  • Aurélien Geron: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts,
  • Tools, and Techniques to Build Intelligent Systems O’Reilly Media, Inc. 2017. ISBN: 1491962291
  • Josh Patterson and Adam Gibson: Deep Learning: A Practitioner's Approach. O’Reilly Media, Inc. 2017. ISBN: 1491914254
  • Sayan Pathak, Roland Fernandez, and Jonathan Sanito, Deep Learning Explained, MOOC edX, explained-microsoft- dat236x