Fundamentals of Deep Learning
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10249842" target="_blank" >RIV/61989100:27740/22:10249842 - isvavai.cz</a>
Výsledek na webu
<a href="https://events.it4i.cz/event/128/" target="_blank" >https://events.it4i.cz/event/128/</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Fundamentals of Deep Learning
Popis výsledku v původním jazyce
Deep Learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software. In this workshop, participants learned how deep learning works through hands-on exercises in computer vision and natural language processing. they have trained deep learning models from scratch, learning tools and tricks to achieve highly accurate results. They have also learned to leverage freely available, state-of-the-art pre-trained models to save time and get their deep learning application up and running quickly.
Název v anglickém jazyce
Fundamentals of Deep Learning
Popis výsledku anglicky
Deep Learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software. In this workshop, participants learned how deep learning works through hands-on exercises in computer vision and natural language processing. they have trained deep learning models from scratch, learning tools and tricks to achieve highly accurate results. They have also learned to leverage freely available, state-of-the-art pre-trained models to save time and get their deep learning application up and running quickly.
Klasifikace
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)
Návaznosti výsledku
Projekt
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Návaznosti
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů