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null yandex_2017
Deep neural networks
Date: 02.02.2017 9:00 - 04.02.2017 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: Andrey Ustyuzhanin (Yandex)
Maxim Borisyak (Yandex)
Mikhail Usvyatsov (Yandex)
Alexander Panin (Yandex)

  • 120 for-finnish-academics
  • 120 for-others
The fee covers all materials, lunches as well as morning and afternoon coffees.
The course is full and registration is no longer possible. The seats are filled in the registration order. You may cancel your attendance without a charge 5 business days prior the course. For cancellations after that and no-shows without a cancellation the full fee will be invoiced.
Additional Information



This course is organized by Yandex School of Data Analysis, Higher School of Economics (, and CSC and gives an introduction to deep learning, convolutional and recurrent neural networks, GPU computing, and commonly used tools to train and apply deep neural networks for various applications.

The course consists of lectures and hands-on exercises.  The main tools in the exercise will be Theano and Lasagne. CSC's Taito-GPU environment will be used in the exercise sessions.

Learning outcome

After the course the participants should have the skills needed for 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 or machine learning.  Note that this is a more advanced course and fundamentals of machine learning are not covered on this course.  In addition, fluent operation in a Linux/Unix environment will be assumed.




Course repository:

Day 1: Introduction to deep learning and tools

9:00 - 10:30 Lecture: Introduction, linear models, stochastic gradient descent

10:30 - 12:00 Seminar

12:00 - 13:30 Lunch

13:30 - 15:00 Lecture: Basic deep / representation learning, backpropagation, initialization, philosophy

15:00 - 16:30 Seminar

16:30 - 18:00 Extra: Yandex intro

Day 2: Convolutional Neural Networks

9:00 - 10:30 Lecture: Computer vision, convolutional neural networks (CNN), batch normalization, data augmentation

10:30 - 12:00 Seminar

12:00 - 13:30 Lunch

13:30 - 15:00 Lecture: Natural language processing, embedding, text CNN

15:00 - 16:30 Seminar

16:30 - 18:00 Extra: Generative adversial networks (GAN)

Day 3: Recurrent Neural Networks

9:00 - 10:30 Lecture: Recurrent neural nets, backpropagation through time, gradient explosion/vanishing

10:30 - 12:00 Seminar

12:00 - 13:30 Lunch

13:30 - 15:00 Lecture: Captioning

15:00 - 16:30 Seminar

16:30 - 18:00 Extra: Cool use cases