Introduction to Deep Learning with PyTorch
The result's identifiers
Result code in 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>
Result on the web
<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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Alternative languages
Result language
angličtina
Original language name
Introduction to Deep Learning with PyTorch
Original language description
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.
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
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů