BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Ecosystem for Research Networking - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Ecosystem for Research Networking
X-ORIGINAL-URL:https://ern.ci
X-WR-CALDESC:Events for Ecosystem for Research Networking
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220325T020000
DTEND;TZID=America/New_York:20220325T170000
DTSTAMP:20260421T075014
CREATED:20220303T191953Z
LAST-MODIFIED:20220330T200532Z
UID:10000075-1648173600-1648227600@ern.ci
SUMMARY:Deep Learning with Python (Webinar)
DESCRIPTION:Hands-on Training – Deep Learning with Python and Jupyter \n\n\n\n\nEVENT RECORDING\n\n\n\n\n\n\n\n\nDeep Learning (DL) outperforms Machine Learning (ML) in many of the applications related to Computer Vision (CV) and Natural Language Processing (NLP). One of the biggest advantages of DL over ML is that they can automatically extract the important features from the data. With sufficient data and compute power\, DL methods\, in particular the supervised learning methods\, can achieve the prediction accuracies that were not seen before with any other statistical methods in applications related to CV and NLP. \n\n\n\nIn this workshop\, we will go through the basics of artificial neural networks (ANN)\, Convolutional Neural Networks (CNN)\, and Recurrent Neural Networks (RNN)\, and do hands-on training with these DL models to build predictive analytics for image and text data. \n\n\n\nObjective of the workshop \n\n\n\nUnderstand the basics of Artificial Neural Networks (ANN)Prepare image and text data suitable for the neural networksLearn how to apply various DL models such as ANN\, CNN\, and RNNImprove the accuracy of the model with Hyperparameter Optimization\n\n\n\nWhat is needed? Laptop/Desktop with Internet connection \n\n\n\nDuration: 3 hours \n\n\n\nLevel: Intermediate \n\n\n\nProgramming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: Will be provided in the workshop
URL:https://ern.ci/event/deep-learning-with-python-webinar/
END:VEVENT
END:VCALENDAR