BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20231023T152118Z UID:96F4F44C-C581-4EC4-B3AF-BA1164FC99EA DTSTART;TZID=US/Eastern:20231021T090000 DTEND;TZID=US/Eastern:20231021T123000 DESCRIPTION:Registration will close Thursday\, October 19th at 5:00PM\n\nCo urse Format: Live Webinar\, 3.5 hours of instruction! Series Overview: Fro m the book introduction: “Neural networks and deep learning currently pr ovides the best solutions to many problems in image recognition\, speech r ecognition\, and natural language processing.”\n\nThis Part 1 and the pl anned Part 2\, (to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning.\n\nThis is a live instructor-led introductory course on Neural Networks and Deep Learning. I t is planned to be a two-part series of courses. The first course is compl ete by itself and covers a feedforward neural network (but not convolution al neural network in Part 1). It will be a pre-requisite for the planned P art 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael N ielsen\, plus additional material such as some proofs of fundamental equat ions not provided in the book.\n\nMore from the book introduction: Referen ce book: “Neural Networks and Deep Learning” by Michael Nielsen\, http ://neuralnetworksanddeeplearning.com/ “We’ll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits. …it can be solved pretty well using a simple neural network\, with just a few tens of lines of code\, and no special libraries.”\n\n“But you don’ t need to be a professional programmer.”\n\nThe code provided is in Pyth on\, which even if you don’t program in Python\, should be easy to under stand with just a little effort.\n\nBenefits of attending the series:\n* L earn the core principles behind neural networks and deep learning.\n* See a simple Python program that solves a concrete problem: teaching a compute r to recognize a handwritten digit.\n* Improve the result through incorpor ating more and more core ideas about neural networks and deep learning.\n* Understand the theory\, with worked-out proofs of fundamental\n\nPre-requ isites: There is some heavier mathematics in learning the four fundamental equations behind backpropagation\, so a basic familiarity with multivaria ble calculus and matrix algebra is expected\, but nothing advanced is requ ired. (The backpropagation equations can be also just accepted without bot hering with the proofs since the provided Python code for the simple netwo rk just make use of the equations.) Basic familiarity with Python or simil ar computer language.\n\nSpeaker(s): CL Kim\, \n\nBoston\, Massachusetts\, United States\, Virtual: https://events.vtools.ieee.org/m/359296 LOCATION:Boston\, Massachusetts\, United States\, Virtual: https://events.v tools.ieee.org/m/359296 ORGANIZER:k.safina@ieee.org SEQUENCE:3 SUMMARY:Introduction to Neural Networks and Deep Learning (Part I) URL;VALUE=URI:https://events.vtools.ieee.org/m/359296 X-ALT-DESC:Description: <br /><p><strong>Registration will close Thursday\, October 19th at 5:00PM</strong></p>\n<p>Course Format:&nbsp\; &nbsp\;Live Webinar\, 3.5 hours of instruction! Series Overview: &nbsp\; From the boo k introduction: &ldquo\;Neural networks and deep learning currently provid es the best solutions to many problems in image recognition\, speech recog nition\, and natural language processing.&rdquo\;<br /><br />This Part 1 a nd the planned Part 2\, (to be confirmed) series of courses will teach man y of the core concepts behind neural networks and deep learning.</p>\n<p>T his is a live instructor-led introductory course on Neural Networks and De ep Learning. It is planned to be a two-part series of courses. The first c ourse is complete by itself and covers a feedforward neural network (but n ot convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the h ighly-regarded and free online book &ldquo\;Neural Networks and Deep Learn ing&rdquo\; by Michael Nielsen\, plus additional material such as some pro ofs of fundamental equations not provided in the book.<br /><br />More fro m the book introduction: &nbsp\;Reference book: &ldquo\;Neural Networks an d Deep Learning&rdquo\; by Michael Nielsen\, <a href="http://neuralnetwork sanddeeplearning.com/" target="_blank" rel="noopener" data-saferedirecturl ="https://www.google.com/url?q=http://neuralnetworksanddeeplearning.com/&a mp\;source=gmail&amp\;ust=1668716871780000&amp\;usg=AOvVaw2YFKDZOE9rV3h7YP 9bcQcS">http://<wbr />neuralnetworksanddeeplearning.<wbr />com/</a> &ldquo \;We&rsquo\;ll learn the core principles behind neural networks and deep l earning by attacking a concrete problem: the problem of teaching a compute r to recognize handwritten digits. &hellip\;it can be solved pretty well u sing a simple neural network\, with just a few tens of lines of code\, and no special libraries.&rdquo\;<br /><br />&ldquo\;But you don&rsquo\;t nee d to be a professional programmer.&rdquo\;<br /><br />The code provided is in Python\, which even if you don&rsquo\;t program in Python\, should be easy to understand with just a little effort.<br /><br />Benefits of atten ding the series:<br />* Learn the core principles behind neural networks a nd deep learning.<br />* See a simple Python program that solves a concret e problem: teaching a computer to recognize a handwritten digit.<br />* Im prove the result through incorporating more and more core ideas about neur al networks and deep learning.<br />* Understand the theory\, with worked- out proofs of fundamental&nbsp\;</p>\n<p>Pre-requisites: There is some hea vier mathematics in learning the four fundamental equations behind backpro pagation\, so a basic familiarity with multivariable calculus and matrix a lgebra is expected\, but nothing advanced is required. (The backpropagatio n equations can be also just accepted without bothering with the proofs si nce the provided Python code for the simple network just make use of the e quations.) Basic familiarity with Python or similar computer language.</p> \n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR