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DTSTAMP:20250710T012228Z
DESCRIPTION:Overview:\n\nIn this 3h tutorial\, participants will be briefly
  introduced to (i) the idea of using neural networks to conduct classifica
 tion\, (ii) practical experience of creating a neural network model using 
 availableТ torchТ modules in R\, (iii) the configuration of hyperparameters 
 of neural networks and their influence on the model performance\, and (iv)
  overfitting issue.Т  This tutorial focuses on the basic ideas of neural ne
 tworks and does not involve mathematics derivation heavily. Т \n\nAt the en
 d of the workshop\, participants will be able to:Т \n	Т Т  - Understand the ge
 neral pipeline of conducting classification\, and the concept of activatio
 n function and loss function.Т \n	Т Т  - Understand the concept of hyperparame
 tersТ \n	Т Т  - Understand the concept of overfitting\n\nPrerequisitesТ \n	Т Т  - 
 Understand the classification problem and the general pipeline of conducti
 ng prediction\, including the training and testing procedure\; e.g. from w
 orkshops Introduction to ML in R\, or Fundamentals of ML in Python.Т \n	Т Т  -
  Knowledge of the basics of regression is preferred. Т \n	Т Т  - Knowledge of 
 R and RStudio.Т \n\n\nDate: Wednesday\, 5 April 2023.\n	Time: 11:30 p.m. to 
 2:30 p.m.\n	Location: hybrid (in-person at Burnside Hall 1104\, and online 
 via Zoom).\n	Instructor: Qihuang Zhang\, assistant professor of Epidemiolog
 y\, Biostatistics\, and Occupational Health (EBOH) at КкСЯВЛДђьШ University.\n
 \n\nТ \n\nRegister\n\nТ \n
DTSTART:20230405T163000Z
DTEND:20230405T183000Z
SUMMARY:Neural Networks in R
URL:/cdsi/channels/event/neural-networks-r-346476
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