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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Canada/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
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DTSTART:20201101T010000
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BEGIN:VEVENT
DTSTAMP:20210119T153740Z
UID:4A80BB5E-8FA4-4351-8031-BD6828CAB54C
DTSTART;TZID=Canada/Eastern:20210205T100000
DTEND;TZID=Canada/Eastern:20210205T120000
DESCRIPTION:This workshop focuses on how to classify or label text using bi
 -LSTM RNNs. It includes pre-processing/cleaning of the text and handling s
 everely imbalanced classes using SMOTE\, oversampling\, under-sampling\, c
 lass count\, and log smoothen weights. Using different types of LSTM such 
 as vanilla LSTM\, and Bi-LSTM\, we focus on time series problems with cate
 gorical data. In summary\, this workshop will cover:\n\na) Preprocessing t
 ext and data\n\nb) Handling imbalanced datasets\n\nc) Use different types 
 of LSTMs for text and time series classification\n\nd) Produce meaningful 
 classification reports\n\nSpeaker(s): Enas Tarawneh\, \n\nVirtual: https:/
 /events.vtools.ieee.org/m/257245
LOCATION:Virtual: https://events.vtools.ieee.org/m/257245
ORGANIZER:Hina.Tabassum@lassonde.yorku.ca
SEQUENCE:0
SUMMARY:Project-based Python Workshop 3
URL;VALUE=URI:https://events.vtools.ieee.org/m/257245
X-ALT-DESC:Description: &lt;br /&gt;&lt;blockquote&gt;\n&lt;div dir=&quot;ltr&quot;&gt;\n&lt;div&gt;\n&lt;div&gt;\n
 &lt;blockquote&gt;\n&lt;div dir=&quot;ltr&quot;&gt;\n&lt;div&gt;\n&lt;div&gt;\n&lt;div dir=&quot;ltr&quot;&gt;\n&lt;div dir=&quot;lt
 r&quot;&gt;\n&lt;div&gt;\n&lt;div&gt;\n&lt;p&gt;This workshop focuses on how to classify or label&amp;nb
 sp\;text using bi-LSTM RNNs. It includes pre-processing/cleaning of the te
 xt and handling severely imbalanced classes using SMOTE\, oversampling\, u
 nder-sampling\, class count\, and log smoothen weights. Using different ty
 pes of LSTM such as vanilla LSTM\, and Bi-LSTM\, we focus on time series p
 roblems with categorical data.&amp;nbsp\; In summary\, this workshop will cove
 r:&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;a) Preprocessing text&amp;nbsp\;and data&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;b) H
 andling imbalanced datasets&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;c) Use different types of LSTMs
  for text and time series classification&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;d) Produce meaning
 ful classification reports&amp;nbsp\;&lt;/p&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;/d
 iv&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;/blockquote&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;/div&gt;\n&lt;/blockquote&gt;
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