Fundamentals of Deep Learning for Multi-GPUs (PTC Course)

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Fundamentals of Deep Learning for Multi-GPUs (PTC Course)

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Fundamentals of Deep Learning for Multi-GPUs (PTC Course)

Popis výsledku anglicky

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

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů