Practical Deep Learning

This course gives a practical introduction to deep learning, including common neural network architectures such as convolutional, recurrent and transformer models. Also covered are 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 using PyTorch. Participants will get to run exercises on the GPU-accelerated LUMI – Europe’s fastest supercomputer!

The course will be held in hybrid mode, both online (Zoom) and on-site participation in Espoo, Finland are possible.

This course is partly funded by the EuroCC 2 project. Therefore, the course is open and free of charge for participants from academia, industry, and public administration from the EuroCC countries. Please register with your organisation’s email address to prove your affiliation. Lunch is not included, but coffee will be served for on-site participants.

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.

Course contents

• Introduction to deep learning
• Using Jupyter Notebooks course environment
• Multi-layer perceptrons
• Image data and convolutional neural networks
• Text data and recurrent and transformers neural networks
• Running jobs on the LUMI supercomputer
• Using multiple GPUs and multiple nodes
• Exercises using PyTorch

A more detailed agenda will appear later.

Lecturers

• Markus Koskela, CSC
• Mats Sjöberg, CSC

Registration

A maximum of 50 people can register for the course, with a maximum of 24 on premise.