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.
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 and content level
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.
Day 1, Thursday 10.2.2022
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, Friday 11.2.2022
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)
Price: Free of charge (2 training days)
READ MORE AND REGISTER at the PRACE TRAINING PORTAL:
REGISTRATION DEADLINE IS JANUARY 30, 2022
REGISTRATION is OBLIGATORY since the details to access the online course will be provided to the registered and accepted attendees only. 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 email@example.com