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Introduction to deep learning
Date: 17.04.2018 9:00 - 18.04.2018 17:00
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-language
lecturers: Markus Koskela
Tommi Jalkanen
  • 120 for-finnish-academics
  • 560 for-others
The fee covers all materials, lunches as well as morning and afternoon coffees.
The course is full. If you are interested in upcoming instances of the course, please add yourself to the waiting list or send email to The seats are filled in the registration order. If a cancellation is received five (5) business days prior to the course, the course fee will be refunded with the exception of a handling fee of 10 €. For no-shows and cancellations after the cut of date no refunds will be made. Registration can be transferred to someone else from the same organization without additional charge.

Payment can be made with electronic invoicing, credit card, or direct bank transfer. Note that for electronic invoicing you need the operator and e-invoicing address (OVT code) of your organization. Please also note that invoice reference is needed for electronic invoicing in your organization, so please have this available when registering.
Additional Information



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 TensorFlow 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 needed to begin applying deep learning for different tasks and utilizing the available GPU capacity 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.  Basics of linear algebra and calculus are sufficient.  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.



Course materials can be found at .

Schedule (changes are possible):

Day 1: Notebooks

9-10 Lecture: Introduction to deep learning
10-11 Exercise: Introduction to Notebooks, Keras fundamentals
11-12 Lecture: Image data, multi-layer percepton networks, convolutional neural networks
12-13 Lunch
13-14 Exercise: Image classification with MLPs, CNNs
14-15 Lecture: Text data, embeddings, neural NLP, recurrent neural networks
15-16 Execise: Text sentiment classification with CNNs, RNNs

Day 2: Taito-GPU

9-10: Lecture: GPUs, batch jobs, using Taito-GPU
10-12: Exercises
12-13 Lunch
13-16: Exercises