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DTSTAMP:20250421T144824Z
UID:FE5F8A33-854E-4684-806B-9763889F9AAD
DTSTART;TZID=America/New_York:20250419T090000
DTEND;TZID=America/New_York:20250419T130000
DESCRIPTION:Series Overview: Neural networks and deep learning currently pr
 ovides the best solutions to many problems in image recognition\, speech r
 ecognition\, natural language processing\, and generative AI.\n\nRegistrat
 ion Fees:\n\nMembers Early Rate (by April 4) $115.00\n\nMembers Rate after
  (April 4) $130.00\n\nNon-Member Early Rate (April 4) $135.00\n\nNon-Membe
 r Rate after (April 4): $150.00\n\nDecision to run or cancel the course is
 : Friday\, April 11th. The Course will run. Course material will be emaile
 d out by the end of the week.\n\nThe Part 1 class and this Part 2 class wi
 ll teach many of the core concepts behind neural networks and deep learnin
 g\, and basic language modeling.\n\nThe planned Part 3 class (to be confir
 med) will teach a simple Generative Pre-trained Transformer (GPT)\, based 
 on the seminal Attention is All You Need paper and OpenAI&#39;s GPT-2/GPT-3.\n
 \nIn this Part 2 class\, in the first section\, we again use a neural netw
 ork in teaching a computer to recognize handwritten digits. Here we introd
 uce the convolutional neural network. They are predominantly used in compu
 ter vision applications\, such as for recognizing objects in images.\n\nTh
 e second section of the Part 2 class introduces basic language modeling\, 
 and simple generation of text based on prior learned text\, in this case\,
  baby names.\n\nBut you don’t need to be a professional programmer. The 
 demo code provided is in Python\, and should be easy to understand with ju
 st a little effort.\n\nBenefits of attending this Part 2 class of the seri
 es:\n\n• Build upon the core principles behind neural networks and deep 
 learning in the Part 1 class to learn about convolutional neural networks.
 \n\n• See a simple Python program that solves a concrete problem: teachi
 ng a computer to recognize a handwritten digit.\n\n• Improve the result 
 through incorporating more and more core ideas about neural networks and d
 eep learning.\n\n• Understand basic language modeling.\n\n• Implement 
 a simple language model that generates baby names from existing names.\n\n
 • Get introduced to the popular PyTorch library.\n\n• Run straightforw
 ard Python demo code examples.\n\nPart 2 class Pre-requisites: The materia
 l in the Part 1 class\, which requires some basic familiarity with multiva
 riable calculus and matrix algebra\, but nothing advanced. Basic familiari
 ty with Python or similar computer language.\n\nSpeaker(s): CL Kim\, \n\nA
 genda: \nBenefits of attending the series:\n• Learn the core principles 
 behind neural networks and deep learning.\n\n• See a simple Python progr
 am that solves a concrete problem: teaching a computer to recognize a hand
 written digit.\n\n• Improve the result through incorporating more and mo
 re core ideas about neural networks and deep learning.\n\n• Understand t
 he theory\, with worked-out proofs of fundamental equations of backpropaga
 tion for those interested.\n\n• Understand basic language modeling.\n\n
 • Run straightforward Python demo code example.\n\nVirtual: https://even
 ts.vtools.ieee.org/m/465524
LOCATION:Virtual: https://events.vtools.ieee.org/m/465524
ORGANIZER:k.safina@ieee.org
SEQUENCE:12
SUMMARY:&quot;Introduction to Neural Networks and Deep Learning (Part 2 - Convol
 utional Neural Networks\, Basic Language Modeling)” 
URL;VALUE=URI:https://events.vtools.ieee.org/m/465524
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;Body&quot;&gt;Series Overview: Neural netwo
 rks and deep learning currently provides the best solutions to many proble
 ms in image recognition\, speech recognition\, natural language processing
 \, and generative AI.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Registration Fees:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;
 Members Early Rate (by April 4) $115.00&lt;/p&gt;\n&lt;p&gt;Members Rate after (April 
 4) $130.00&lt;/p&gt;\n&lt;p&gt;Non-Member Early Rate (April 4) $135.00&lt;/p&gt;\n&lt;p&gt;Non-Mem
 ber Rate after (April 4):&amp;nbsp\; $150.00&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Decision to run o
 r cancel the course is:&amp;nbsp\; &amp;nbsp\;Friday\, April 11th.&amp;nbsp\; &amp;nbsp\; 
 The Course will run.&amp;nbsp\; Course material will be emailed out by the end
  of the week.&amp;nbsp\;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;Body&quot;&gt;The Part 1 class and th
 is Part 2 class will teach many of the core concepts behind neural network
 s and deep learning\, and basic language modeling.&lt;/p&gt;\n&lt;p class=&quot;Body&quot;&gt;Th
 e planned Part 3 class (to be confirmed) will teach a simple Generative Pr
 e-trained Transformer (GPT)\, based on the seminal&amp;nbsp\;&lt;em&gt;Attention is 
 All You Need&lt;/em&gt; paper and OpenAI&#39;s GPT-2/GPT-3.&lt;/p&gt;\n&lt;p class=&quot;Body&quot;&gt;In 
 this Part 2 class\, in the first section\, we again use a neural network i
 n teaching a computer to recognize handwritten digits. Here we introduce t
 he convolutional neural network. They are predominantly used in computer v
 ision applications\, such as for recognizing objects in images.&lt;/p&gt;\n&lt;p cl
 ass=&quot;Body&quot;&gt;The second section of the Part 2 class introduces basic languag
 e modeling\, and simple generation of text based on prior learned text\, i
 n this case\, baby names.&lt;/p&gt;\n&lt;p class=&quot;Body&quot;&gt;But you don&lt;span dir=&quot;RTL&quot; 
 lang=&quot;AR-SA&quot; style=&quot;font-family: &#39;Arial Unicode MS&#39;\,serif\; mso-ascii-fon
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 squo\;&lt;/span&gt;t need to be a professional programmer. The demo code provide
 d is in Python\, and should be easy to understand with just a little effor
 t.&lt;/p&gt;\n&lt;p class=&quot;Body&quot;&gt;Benefits of attending this Part 2 class of the ser
 ies:&lt;/p&gt;\n&lt;p class=&quot;Body&quot; style=&quot;margin-left: 9.0pt\; text-indent: -9.0pt\
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 &lt;/span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;Build upon the core principles behind neural n
 etworks and deep learning in the Part 1 class to learn about convolutional
  neural networks.&lt;/p&gt;\n&lt;p class=&quot;Body&quot; style=&quot;margin-left: 9.0pt\; text-in
 dent: -9.0pt\; mso-list: l1 level1 lfo2\;&quot;&gt;&lt;!-- [if !supportLists]--&gt;&lt;span
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 digit.&lt;/p&gt;\n&lt;p class=&quot;Body&quot; style=&quot;margin-left: 9.0pt\; text-indent: -9.0p
 t\; mso-list: l1 level1 lfo2\;&quot;&gt;&lt;!-- [if !supportLists]--&gt;&lt;span style=&quot;mso
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 n&gt;&lt;/span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;Improve the result through incorporating mor
 e and more core ideas about neural networks and deep learning.&lt;/p&gt;\n&lt;p cla
 ss=&quot;Body&quot; style=&quot;margin-left: 9.0pt\; text-indent: -9.0pt\; mso-list: l1 l
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 =&quot;margin-left: 9.0pt\; text-indent: -9.0pt\; mso-list: l1 level1 lfo2\;&quot;&gt;&lt;
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 &#39;Times New Roman&#39;\;&quot;&gt;&amp;nbsp\;&amp;nbsp\; &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;Imp
 lement a simple language model that generates baby names from existing nam
 es.&lt;/p&gt;\n&lt;p class=&quot;Body&quot; style=&quot;margin-left: 9.0pt\; text-indent: -9.0pt\;
  mso-list: l1 level1 lfo2\;&quot;&gt;&lt;!-- [if !supportLists]--&gt;&lt;span style=&quot;mso-ha
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 /span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;Get introduced to the popular PyTorch library.&lt;
 /p&gt;\n&lt;p class=&quot;Body&quot; style=&quot;margin-left: 9.0pt\; text-indent: -9.0pt\; mso
 -list: l1 level1 lfo2\;&quot;&gt;&lt;!-- [if !supportLists]--&gt;&lt;span style=&quot;mso-hansi-
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 n&gt;&lt;/span&gt;&lt;!--[endif]--&gt;Run straightforward Python demo code examples.&lt;/p&gt;\
 n&lt;p class=&quot;Body&quot;&gt;Part 2 class Pre-requisites: The material in the&lt;span lan
 g=&quot;IT&quot; style=&quot;mso-ansi-language: IT\;&quot;&gt; Part 1 class&lt;/span&gt;\, which requir
 es some basic familiarity with multivariable calculus and matrix algebra\,
  but nothing advanced. Basic familiarity with Python or similar computer l
 anguage.&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;pre style=&quot;background: white\;&quot;&gt;&lt;sp
 an style=&quot;font-size: 12.0pt\; font-family: &#39;Arial&#39;\,sans-serif\; mso-farea
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  mso-ligatures: none\;&quot;&gt;Benefits of attending the series:&lt;/span&gt;&lt;/pre&gt;\n&lt;p
  class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0in\; line-height: normal\; tab-s
 tops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2p
 t 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt\; background: wh
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 ing: 0pt\; mso-ligatures: none\;&quot;&gt;&amp;bull\; Learn the core principles behind
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 =&quot;margin-bottom: 0in\; line-height: normal\; tab-stops: 45.8pt 91.6pt 137.
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 lass=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0in\; line-height: normal\; tab-sto
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 rating more and more core ideas about neural networks and deep learning.&lt;/
 span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0in\; line-height: n
 ormal\; tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 3
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 r those interested.&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom:
  0in\; line-height: normal\; tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.
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 7.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.
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 es: none\;&quot;&gt;&amp;bull\; Run straightforward Python demo code example.&lt;/span&gt;&lt;/
 p&gt;
END:VEVENT
END:VCALENDAR