AI and HPC
The term AI is nowadays often used interchangeably with Deep Neural Networks, a technology which is currently changing society as well as science and technology. There is a strong connection between AI and High Performance Computing (HPC) in that AI workloads often need to be, or are run on, HPC systems. Such workloads also turn out to be a particularly challenging application of HPC.
Praktische info:
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Leertraject
This hybrid (on location/online) course is at the beginners/intermediate level. Some of the basic theory and models in deep learning will be covered as well as the techniques involved when running such models. The course includes practical exercises to deepen the understanding of the lectures. At the end of the course participants should be familiar with some of the concepts in deep learning, be able to run simple AI workloads and run them in parallel on an HPC system.
Learning outcomes
After this course, you will
- Be familiar with the history and context of deep learning
- Be familiar with some of the principles of deep learning (backpropagation, training concepts)
- Be familiar with some deep learning architectures (perceptron, DNN, CNN, recurrent, transformer)
- Be familiar with PyTorch and master basic constructs
- Be able to run simpler AI tasks
- Be familiar with an instance of a hybrid HPC architecture (CPU+GPU)
- Be familiar with different methods of parallelizing AI tasks
- Master some aspects of running AI tasks on an HPC system
- Be aware of some of the risks and implications of AI for society
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