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Projects and Collaboration Networks
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Practical Deep Learning
Date: 13.02.2019 9:00 - 14.02.2019 16:00
Location details: The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.
Language: English
Lecturers: Markus Koskela (CSC)
Mats Sjöberg (CSC)
  • Free for Finnish universities, universities of applied sciences and governmental research institutes.
  • Free for others.
The course materials, lunches as well as morning and afternoon coffees are free of charge.
THE COURSE IS FULLY BOOKED! If you have registered to this course and you are not able to attend, please cancel your registration in advance by sending an e-mail to

We have processed a LIMITED WAITING LIST (10 persons max). Please send your request to and we will keep those on the waiting list informed of whether participation will be possible. Please note that registration/wait list is at a first come, first served basis.
Additional information
This course is part of the PRACE Training Centres (PTCs) activity. Please visit the PRACE Training portal for further information about the course. For content please contact, for practicalities


This course gives a practical introduction to deep learning, convolutional and recurrent neural networks, GPU computing, and tools to train and apply deep neural networks for natural language processing, images, and other applications.

The course consists of lectures and hands-on exercises. Keras ( and PyTorch ( will be used in the exercise sessions. CSC's Notebooks ( environment will be used on the first day of the course, and the Taito-GPU ( cluster on the second day.

Learning outcome

After the course the participants should have the skills and knowledge needed to begin applying deep learning for different tasks and utilizing the GPU resources available at CSC for training and deploying their own neural networks.


The participants are assumed to have working knowledge of Python and suitable background in data analysis, machine learning, or a related field. Previous experience in deep learning is not required, but the fundamentals of machine learning are not covered on this course.  Basic knowledge of a Linux/Unix environment will be assumed.



Agenda (tentative)

Day 1, Wednesday 13.2

  •    09.00 – 10.30 Lecture: Introduction to deep learning

  •    10.30 – 11.00 Exercises: Introduction to Notebooks, Keras fundamentals

  •    11.00 – 12.00 Lecture: Image data, multi-layer percepton networks, convolutional neural networks

  •    12.00 – 13.00 Lunch

  •    13.00 – 14.00 Exercises: Image classification with MLPs, CNNs

  •    14.00 – 15.00 Lecture: Text data, embeddings, neural NLP, recurrent neural networks

  •    15.00 – 16.00 Exercises: Text sentiment classification with CNNs, RNNs

Day 2, Thursday 14.2

  •    09.00 – 10.00 Lecture: GPUs, batch jobs, using Taito-GPU

  •    10.00 – 12.00 Exercises: Image classification

  •    12.00 – 13.00 Lunch

  •    13.00 – 14.00 Exercises: Text categorization and labelling

  •    14.00 – 15.00 Lecture: Cloud, GPU utilization, multiple GPUs

  •    15.00 – 16.00 Exercises: Using multiple GPUs

Coffee will be served both for the morning and afternoon sessions

Projects and Collaboration Networks
Service Break
Projects and Collaboration Networks
Service Break