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SUMMARY:[Online] Fundamentals of Deep Learning
DTSTART:20260911T070000Z
DTEND:20260911T150000Z
DTSTAMP:20260724T012100Z
UID:indico-event-207@indico.ecap.work
DESCRIPTION:Fundamentals of Deep Learning\n\nSchedule & Format\n\nDate: 20
 26\, September 11\nTime: 9:00 - 17:00 CE(S)T\nFormat: Full-day\nLocation: 
 Online via Zoom\nLanguage: English\n\nRegistered participants will receive
  the video conferencing link via email on the day before the course.\nInst
 ructor\n\nChang Liu\, NHR@FAU\, certified NVIDIA DLI Ambassador\n\nThis co
 urse is organized by Erlangen National High Performance Computing Center (
 NHR@FAU) in collaboration with NVIDIA Deep Learning Institute (DLI).\nCour
 se Description\nDeep learning powers many of today's most impactful AI app
 lications\, from image recognition to large language models. This NVIDIA D
 LI course provides a practical introduction to the field through hands-on 
 exercises in computer vision and natural language processing. Participants
  learn to build and train neural networks from scratch\, apply data augmen
 tation and regularization to improve accuracy\, and make use of state-of-t
 he-art pre-trained models via transfer learning.\nFurther information abou
 t this tutorial can be found on the NVIDIA DLI course page.\nPrerequisites
 \nKnowledge\n\nPython 3 programming experience\, including functions\, loo
 ps\, dictionaries\, and arrays\nFamiliarity with Pandas data structures an
 d basic statistics (e.g.\, computing a regression line)\n\nTechnical\n\nA 
 free NVIDIA developer account\n\nCourse Structure\n\nMechanics of deep lea
 rning: training a first model\, convolutional neural networks\, data augme
 ntation\nPre-trained models and large language models: image classificatio
 n with transfer learning\, LLMs for text tasks\nFinal project: color image
  classification with small datasets\, combining transfer learning and feat
 ure extraction\n\nLearning Outcomes\nAfter completing this course\, you wi
 ll be able to:\n\nBuild and train neural networks from scratch for compute
 r vision tasks\nApply convolutional neural network (CNN) architectures to 
 image classification problems\nUse data augmentation techniques to improve
  model generalization with limited data\nLeverage transfer learning with p
 re-trained models to achieve strong results efficiently\nApply pre-trained
  large language models to text-based question answering tasks\nDesign and 
 execute a complete deep learning project from data preparation to model ev
 aluation\n\nRegistration\, Wait List and Withdrawal Policy\nRegistration\n
 Please register at the bottom of this page. Registration is open until a f
 ew days before the course starts\, or until the course is fully booked.\nP
 rices and Eligibility\nThis course is open and free of charge for particip
 ants affiliated with academic institutions in European Union (EU) member s
 tates and Horizon 2020-associated countries.\nWait List\nIf the course rea
 ches its maximum capacity\, you can request to join the wait list by sendi
 ng an email to nhr-training@fau.de. Please include your name and universit
 y affiliation in the message.\nWithdrawal Policy\nPlease only register if 
 you are committed to attending the course. No-shows will be blacklisted an
 d excluded from future events.\nIf you need to withdraw your registration\
 , please either cancel it directly through the registration system or send
  an email to nhr-training@fau.de.\nAdditional Courses\nYou can find an up-
 to-date list of all courses offered by NHR@FAU at https://hpc.fau.de/teach
 ing/tutorials-and-courses/.\n\nhttps://indico.ecap.work/event/207/
LOCATION:Online
URL:https://indico.ecap.work/event/207/
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