BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Denver
BEGIN:DAYLIGHT
DTSTART:20260308T030000
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:MDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20261101T010000
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:MST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260727T041911Z
UID:30D8AEB1-735E-4A77-9BCF-0E0327190FDC
DTSTART;TZID=America/Denver:20260723T183000
DTEND;TZID=America/Denver:20260723T200000
DESCRIPTION:The presentation provides a sweeping introduction to neural net
 works\, covering fundamental concepts from biological inspiration to mathe
 matical implementation. Topics include an overview of network architecture
 s\, activation functions\, forward propagation\, Backpropagation\, and tra
 ining techniques. Attendees will gain a foundational understanding of how 
 Multilayer Perceptron (MLP) and deep networks learn complex patterns from 
 data.\n\n- Fundamental Concepts: Introduction to artificial neurons\, weig
 hts\, biases\, and the structure of layers (input\, hidden\, output)\n- Ne
 twork Operations: How neural networks compute outputs through forward prop
 agation\n- Learning Mechanisms: Training\, loss functions\, and the Backpr
 opagation\n- Non-linearity and Activation Functions: ReLU\, Sigmoid\, and 
 Tanh functions\n- Real-World Inferencing Applications: Sample of use-case 
 scenarios\n\nSpeaker(s): Randy Rannow\n\nDowntown Paninos - 604 North Tejo
 n Street\, Colorado Springs\, Colorado\, United States\, Virtual: https://
 events.vtools.ieee.org/m/566622
LOCATION:Downtown Paninos - 604 North Tejon Street\, Colorado Springs\, Col
 orado\, United States\, Virtual: https://events.vtools.ieee.org/m/566622
ORGANIZER:smehalingam@binghamton.edu
SEQUENCE:38
SUMMARY:Neural Networks: A Comprehensive Introduction
URL;VALUE=URI:https://events.vtools.ieee.org/m/566622
X-ALT-DESC:Description: <br /><p>The presentation provides a sweeping intro
 duction to neural networks\, covering fundamental concepts from biological
  inspiration to mathematical implementation. Topics include an overview of
  network architectures\, activation functions\, forward propagation\, Back
 propagation\, and training techniques. Attendees will gain a foundational 
 understanding of how Multilayer Perceptron (MLP) and deep networks learn c
 omplex patterns from data.</p>\n<ul>\n<li>Fundamental Concepts: &nbsp\;Int
 roduction to artificial neurons\, weights\, biases\, and the structure of 
 layers (input\, hidden\, output)</li>\n<li>Network Operations: &nbsp\;How 
 neural networks compute outputs through forward propagation</li>\n<li>Lear
 ning Mechanisms: &nbsp\;Training\, loss functions\, and the Backpropagatio
 n</li>\n<li>Non-linearity and Activation Functions: &nbsp\;ReLU\, Sigmoid\
 , and Tanh functions</li>\n<li>Real-World Inferencing Applications: &nbsp\
 ;Sample of use-case scenarios</li>\n</ul>
END:VEVENT
END:VCALENDAR