Fundamentals of Deep Learning for Multi-GPUs (PTC Course)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F20%3A10248906" target="_blank" >RIV/61989100:27740/20:10248906 - isvavai.cz</a>
Result on the web
<a href="https://events.it4i.cz/event/110/" target="_blank" >https://events.it4i.cz/event/110/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Fundamentals of Deep Learning for Multi-GPUs (PTC Course)
Original language description
The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU or even years for larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible. Attendees learned how to use multiple GPUs to train neural networks. They also learned: Approaches to multi-GPUs training Algorithmic and engineering challenges to large-scale training Key techniques used to overcome the challenges mentioned above
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů