Thursday, June 15
Section outline
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09:00-16:00 Data Parallel Neural Networks
Location: Paderborn Center for Parallel Computing (PC2), Room E5.333
Lecturers: Dr. Charlotte Debus (KIT), Dr. Marie Weiel (KIT), David Li (KIT)
Materials https://github.com/mcw92/nhr-summerschool
Agenda
- 09:00 - 10:15 - Introduction to Neural Networks
- Backpropagation and Stochastic Gradient Descent
- Layer Architectures
- Training a Neural Network
- 10:30 - 12:00 - Hands-on Session
- Neural Networks with PyTorch
- 12:00 - 13:00 - Lunch break
- 13:00 - 14:15 - Data-parallel Neural Networks
- Parallelisation Strategies for Neural Networks
- Distributed SGD
- IID and Large Minibatch Effects
- 14:30 - 16:00 - Hands-on Session
- PyTorch Distributed Dataparallel
- 16.00 Walk into the City to the LWL Museum
Prerequisites / Preparation:
You should be familiar with basic Python and Jupyter. A basic understanding of machine-learning techniques and gradient-based optimisation is beneficial. Furthermore you should have basic knowledge on distributed computing and communication via MPI.
From 17:00 Archäologische Spurensuche
Start and End: LWL Museum Kaiserpfalz, Paderborn (View on map)
Language: English
After that dinner together at Kitzgams, Paderborn (View on map)
- 09:00 - 10:15 - Introduction to Neural Networks