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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20201014T103211Z
UID:EB2CAF1E-85BA-4819-B3C3-6E31D8A0A30E
DTSTART;TZID=Asia/Kolkata:20200810T100000
DTEND;TZID=Asia/Kolkata:20200810T110000
DESCRIPTION:A deep learning model is designed to continually analyze data w
 ith a logic structure similar to how a human would draw conclusions. To ac
 hieve this\, deep learning applications use a layered structure of algorit
 hms called an artificial neural network. The design of an artificial neura
 l network is inspired by the biological neural network of the human brain\
 , leading to a process of learning that’s far more capable than that of 
 standard machine learning models.\n\nCo-sponsored by: IEEE CIS SOCIETY\, I
 EEE SVIT EMBS Society and Department of ECE SVIT\n\nSpeaker(s): Dr.Prabuch
 andran K.J\, \n\nVirtual: https://events.vtools.ieee.org/m/243117
LOCATION:Virtual: https://events.vtools.ieee.org/m/243117
ORGANIZER:venkatesha.m@saividya.ac.in
SEQUENCE:0
SUMMARY:Machine Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/243117
X-ALT-DESC:Description: <br /><p>A deep learning model is designed to conti
 nually analyze data with a logic structure similar to how a human would dr
 aw conclusions. To achieve this\, deep learning applications use a layered
  structure of algorithms called an <strong>artificial neural network</stro
 ng>. The design of an artificial neural network is inspired by the biologi
 cal neural network of the human brain\, leading to a process of learning t
 hat&rsquo\;s far more capable than that of standard machine learning model
 s.</p>
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