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practical_deep_learning_dec-2019

Practical Deep Learning
Date: 12.12.2019 9:00 - 13.12.2019 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-language
lecturers: Markus Koskela (CSC)
Mats Sjöberg (CSC)
Price:
  • free-price-finnish-academics.
  • free-price-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 patc@csc.fi

We have processed a LIMITED WAITING LIST. Please send your request to patc@csc.fi 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 Centre activity, please visit the PRACE Training portal for further information about the course.
For content please contact: mats.sjoberg@csc.fi
Practicalities: patc@csc.fi

Description

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 new Puhti-AI partition 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.

Prerequisites

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.

Program

Day 1, Thursday 12.12

  •    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, recurrent neural networks, and attention

Day 2, Friday 13.12

  •    09.00 – 10.30 Deep learning frameworks, GPUs, batch jobs

  •    10.30 – 12.00 Image classification exercises

  •    12.00 – 13.00 Lunch

  •    13.00 – 14.00 Text categorization exercises

  •    14.00 – 16.00 Cloud, using multiple GPUs

Coffee will be served both for the morning and afternoon sessions

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