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SUMMARY:Τraining modules for SMEs - Module 10 "Multiprocessing in Python"
DTSTART;VALUE=DATE-TIME:20250714T090000Z
DTEND;VALUE=DATE-TIME:20250714T110000Z
DTSTAMP;VALUE=DATE-TIME:20260618T042835Z
UID:indico-event-178@events.grnet.gr
DESCRIPTION:\n\n\n\nGRNET announces\, in the context of SmartAttica EDIH (
 European Digital Innovation Hub)\, the 10th Module of Τraining modules fo
 r SMEs  with the subject "Multiprocessing in Python". \n\nDate: July 14th
 \, 2025\n\nLocation: Online via Zoom \n\nPresentation Languages: Greek\n
 \nInstructor: Dr. Nikolaos Bakas\, Panagiota Gyftou\n\nDescription: Join u
 s for an insightful seminar where we delve into the world of high-performa
 nce computing (HPC) with a focus on leveraging multiprocessing techniques 
 and hyperparameter tuning. This session is designed to equip professionals
  with the skills to optimize computational tasks\, particularly in machine
  learning applications\, using Python's multiprocessing capabilities. We w
 ill explore practical examples\, including the parallel execution of Rando
 m Forest models\, to demonstrate how to efficiently utilize computational 
 resources.\n\nTarget Audience: This seminar is tailored for professionals 
 from small and medium-sized enterprises (SMEs) who are involved in data sc
 ience\, machine learning\, or any computationally intensive tasks. It is i
 deal for those looking to enhance their understanding of parallel computin
 g and improve the performance of their machine learning models.\n\nLearnin
 g Objectives:\n\n\n	\n	Understand the fundamentals of multiprocessing in P
 ython and its application in HPC environments.\n	\n	\n	Learn how to implem
 ent parallel processing to optimize machine learning workflows.\n	\n	\n	Ga
 in insights into hyperparameter tuning techniques to improve model perform
 ance.\n	\n	\n	Explore practical examples of using multiprocessing for Rand
 om Forest model training and evaluation.\n	\n\n\nPrerequisites: Participan
 ts should have a basic understanding of Python programming and familiarity
  with machine learning concepts. Prior experience with Random Forest model
 s and basic knowledge of parallel computing will be beneficial but not req
 uired.\n\nIndicative Contents:\n\n\n	\n	Introduction to High-Performance C
 omputing (HPC) and its significance in SMEs.\n	\n	\n	Overview of Python's 
 multiprocessing module and its advantages.\n	\n	\n	Step-by-step guide to i
 mplementing parallel processing in machine learning tasks.\n	\n	\n	Case st
 udy: Parallel execution of Random Forest models using multiprocessing.\n	\
 n	\n	Techniques for hyperparameter tuning in HPC environments.\n	\n	\n	Bes
 t practices for optimizing computational resources in SMEs.\n	\n\n\nThe pr
 oject is co-funded by the European Union. Views and opinions expressed are
  however those of the author(s) only and do not necessarily reflect those 
 of the European Union or the European Commission. Neither the European Uni
 on nor the granting authority can be held responsible for them. \n\nhttps:
 //events.grnet.gr/event/178/
LOCATION:
URL:https://events.grnet.gr/event/178/
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