Online: Practical Deep Learning

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. TensorFlow 2, 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 GPU-accelerated Puhti supercomputer 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, Monday 30.8

  •    09.00 – 11.00 Introduction to deep learning and to Notebooks
  •    11.00 – 12.00 Multi-layer perceptrons

               12:00 – 13:00  Lunch

  •    13.00 – 14.30 Image data and convolutional neural networks
  •    14.30 – 16.00 Text data and recurrent neural networks

Day 2, Tuesday 31.8

  •    09.00 – 10.30 Deep learning frameworks, GPUs, batch jobs
  •    10.30 – 12.00 Image classification exercises

               12:00 – 13:00  Lunch

  •    13.00 – 14.30 Attention and text categorization exercises
  •    14.30 – 16.00 Cloud, using multiple GPUs


Markus Koskela (CSC), Katja Mankinen (CSC), Mats Sjöberg (CSC)

Language:  English
Price:          Free of charge (2 training days)

For further detailed information and registration/wait list please visit:

If you have registered to the course and you are not able to attend, please CANCEL your registration in advance by sending your request to