Introduction to Deep Learning with PyTorch

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F25%3A10260381" target="_blank" >RIV/61989100:27740/25:10260381 - isvavai.cz</a>

Výsledek na webu

<a href="https://events.it4i.cz/event/347/" target="_blank" >https://events.it4i.cz/event/347/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Introduction to Deep Learning with PyTorch

Popis výsledku v původním jazyce

This training introduced participants to PyTorch in an HPC environment, covering deep learning, fine-tuning, testing neural network models, and implementing concepts such as distributed data parallelism. Designed for researchers and developers, the course included hands-on sessions to strengthen practical skills. The knowledge and skills gained were highly relevant for professionals across various sectors, as participants learned how to train and deploy neural network models in a high-performance computing environment to address data-intensive tasks such as automated quality inspection, customer behaviour prediction, demand forecasting, and sensor data classification. These techniques supported innovation and improved efficiency in fields including manufacturing, healthcare, finance, agriculture, and logistics, while the hands-on sessions ensured that attendees left with practical tools for developing scalable, AI-driven solutions tailored to their industry needs.

Název v anglickém jazyce

Introduction to Deep Learning with PyTorch

Popis výsledku anglicky

This training introduced participants to PyTorch in an HPC environment, covering deep learning, fine-tuning, testing neural network models, and implementing concepts such as distributed data parallelism. Designed for researchers and developers, the course included hands-on sessions to strengthen practical skills. The knowledge and skills gained were highly relevant for professionals across various sectors, as participants learned how to train and deploy neural network models in a high-performance computing environment to address data-intensive tasks such as automated quality inspection, customer behaviour prediction, demand forecasting, and sensor data classification. These techniques supported innovation and improved efficiency in fields including manufacturing, healthcare, finance, agriculture, and logistics, while the hands-on sessions ensured that attendees left with practical tools for developing scalable, AI-driven solutions tailored to their industry needs.

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

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Návaznosti

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Rok uplatnění

2025

Kód důvěrnosti údajů

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