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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Reprezentace síťových toků s využitím neuronových sítí
 Pycz, Lukasz ; Jeřábek, Kamil (referee) ; Poliakov, Daniel (advisor)
This thesis explores the application of self-supervised learning (SSL) methods such as data masking, data order shuffling, and contrastive learning, to extract meaningful representations from network flow data, specifically using the CESNET TLS22 dataset from CESNET DataZoo. The main goal is to develop a robust model that improves the understanding and analysis of network flows through effective representation learning without relying on labeled data. The research utilizes the PyTorch computational framework for designing, training, and evaluating the performance of the model.
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