Graduate (CUGS, CIS, ...) students interested in the use and the structure of domain-specific programming frameworks for artificial neural networks and deep learning.
Organization and Schedule
The course was last given
This is a new course.
The course studies the programming model, structure, optimization and acceleration opportunities of some popular domain-specific programming frameworks for deep learning, such as TensorFlow.
Programming in C++ and/or Python.
Some background in parallel and accelerator computing, such as TDDD56 or TDDC78.
Data structures and algorithms.
Some introductory course on machine learning including neural networks.
No mandatory course book, but some references are given below.
Background on deep learning, e.g.:
Deep Neural Networks: A Signal Processing Perspective.
Chapter in S. S. Bhattacharyya et al. (eds.), Handbook of Signal Processing Systems, 3rd edition, 2019, pages 133-163.
TensorFlow for Dummies.
Available in the Campus Valla Library as electronic resource.
Christoph Kessler, IDA, Linköpings universitet (course leader, lecturer, examiner)
3hp if both examination moments are fulfilled.
New course 2018.
This course could complement the course Neural Networks and Deep Learning, which is also announced for HT2018.