PhD course (CIS):

Programming Frameworks for Deep Learning (3hp)

Programming Models and Accelerated Learning on Parallel and Heterogeneous Systems


Recommended for
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

As this course focuses on programming and acceleration aspects, we will only shortly summarize the theory of neural networks and recommend to combine this course with another course on the theory of neural networks, such as Neural Networks and Deep Learning.

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.
Linear algebra.
Discrete mathematics.
Data structures and algorithms.
Some introductory course on machine learning including neural networks.



The login and password has been handed out to the course participants in the introductory lecture.


No mandatory course book, but some references are given below.

Background on deep learning, e.g.:

About Tensorflow programming and installation: e.g., Further references: see the slides.

Christoph Kessler, IDA, Linköpings universitet (course leader, lecturer, examiner)


3hp if both examination moments are fulfilled.

New course 2018.

Related Courses
This course could complement the course Neural Networks and Deep Learning, which is also announced for HT2018.

This page is maintained by Christoph Kessler