This course will be delivered as a HYBRID EVENT with participants either on-premise or remote over Zoom.
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 GPU-accelerated LUMI or Puhti supercomputers 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.
Agenda
Day 1, Thursday 24.11
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Introduction to deep learning and to Notebooks
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Multi-layer perceptrons
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Image data and convolutional neural networks
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Text data and recurrent neural networks
Day 2, Friday 25.11
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Deep learning frameworks, GPUs, batch jobs
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Image classification exercises
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Attention and text categorization exercises
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Cloud, using multiple GPUs
Lecturers
Markus Koskela (CSC), Mats Sjöberg (CSC)
Language: English
Price: Free of charge (2 training days) | daily lunch (12:00 - 13:00) as well as morning & afternoon coffee for on-site participants are free of charge.
Registration
Please note that the number of seats for ON-PREMISE participation is limited (24) and registration is at a first come, first served basis. If you have registered to this course and you are not able to attend, please CANCEL your registration in advance by sending an email to patc@csc.fi
