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Programming Frameworks for Deep Learning


Status Active - open for registrations
School Computer and Information Science (CIS)
Division PELAB
Owner Christoph Kessler
Homepage http://www.ida.liu.se/~chrke55/courses/PFDL/index.shtml

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Course plan


Introductory lecture (half day), miniproject, demo/presentation session.
As this course focuses on programming and acceleration aspects, we will only shortly summarize the theory of neural networks and, where applicable, recommend to combine this course with another course on the theory of neural networks, such as Neural Networks and Deep Learning.

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.

The course was last given

This is a new course.


The course studies the programming model, structure, optimization and
acceleration opportunities for some popular domain-specific programming
frameworks for deep learning, such as TensorFlow.


Programming in C++ and/or Python.
Some background in parallel and GPU/accelerator computing, such as TDDD56 or TDDC78.
Linear algebra.
Discrete mathematics.
Data structures and algorithms.
Some introductory course on machine learning including neural networks.


* Short recapitulation of fundamental issues in machine learning, feed-forward neural networks, backpropagation learning algorithm.
* Study of some popular domain-specific programming frameworks for neural networks and deep learning, such as TensorFlow, Caffe, Dlib.
- Programming constructs, Framework structure, Examples. Classification.
* Opportunities and techniques for accelerated learning and execution on parallel systems (multicore, GPGPU).
* Individual miniproject by participants.
Presentation of miniproject results.


To be announced.


Christoph Kessler


Christoph Kessler


* Active participation in the introductory lecture and presentation sessions
* Successful individual miniproject and presentation


3hp if both examination moments are fulfilled.


This course could complement the announced (HT2018) course Neural Networks and Deep Learning, but is not dependent on it.

Page responsible: Director of Graduate Studies