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:20230321T133245Z UID:5A2B0291-0805-43C3-9D2F-CE5F242AD99B DTSTART;TZID=US/Eastern:20230318T090000 DTEND;TZID=US/Eastern:20230318T123000 DESCRIPTION:Course Format: Live Webinar\, 3.5 hours of instruction! Series Overview: From the book introduction: “Neural networks and deep learning currently provides the best solutions to many problems in image recogniti on\, speech recognition\, and natural language processing.”\n\nThis Part 1 and the planned Part 2\, (to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning.\n\nTh is is a live instructor-led introductory course on Neural Networks and Dee p Learning. It is planned to be a two-part series of courses. The first co urse is complete by itself and covers a feedforward neural network (but no t 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 hi ghly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen\, plus additional material such as some proofs of fund amental equations not provided in the book.\n\nMore from the book introduc tion: Reference book: “Neural Networks and Deep Learning” by Michael N ielsen\, [http://neuralnetworks](http://neuralnetworks/) and [deeplearning .com](http://deeplearning.com/) “We’ll learn the core principles behin d neural networks and deep learning by attacking a concrete problem: the p roblem of teaching a computer to recognize handwritten digits. …it can b e 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 Python\, w hich even if you don’t program in Python\, should be easy to understand with just a little effort.\n\nBenefits of attending the series:\n* Learn t he core principles behind neural networks and deep learning.\n* See a simp le Python program that solves a concrete problem: teaching a computer to r ecognize a handwritten digit.\n* Improve the result through incorporating more and more core ideas about neural networks and deep learning.\n* Under stand the theory\, with worked-out proofs of fundamental\n\nPre-requisites : There is some heavier mathematics in learning the four fundamental equat ions behind backpropagation\, so a basic familiarity with multivariable ca lculus and matrix algebra is expected\, but nothing advanced is required. (The backpropagation equations can be also just accepted without bothering with the proofs since the provided Python code for the simple network jus t make use of the equations.) Basic familiarity with Python or similar com puter language.\n\nSpeaker(s): CL Kim\, \n\nBoston\, Massachusetts\, Unite d States\, Virtual: https://events.vtools.ieee.org/m/312967 LOCATION:Boston\, Massachusetts\, United States\, Virtual: https://events.v tools.ieee.org/m/312967 ORGANIZER:k.safina@ieee.org SEQUENCE:9 SUMMARY:Introduction to Neural Networks and Deep Learning (Part I) URL;VALUE=URI:https://events.vtools.ieee.org/m/312967 X-ALT-DESC:Description: <br /><p>Course Format:&nbsp\; &nbsp\;Live Webinar\ , 3.5 hours of instruction! Series Overview: &nbsp\; From the book introdu ction: &ldquo\;Neural networks and deep learning currently provides the be st solutions to many problems in image recognition\, speech recognition\, and natural language processing.&rdquo\;<br /><br />This 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.</p>\n<p>This is a live instructor-led introductory course on Neural Networks and Deep Learni ng. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convol utional neural network in Part 1). It will be a pre-requisite for the plan ned Part 2 second course. The class material is mostly from the highly-reg arded and free online book &ldquo\;Neural Networks and Deep Learning&rdquo \; by Michael Nielsen\, plus additional material such as some proofs of fu ndamental equations not provided in the book.<br /><br />More from the boo k introduction: &nbsp\;Reference book: &ldquo\;Neural Networks and Deep Le arning&rdquo\; by Michael Nielsen\,&nbsp\;<a href="http://neuralnetworks/" >http://neuralnetworks</a>&nbsp\;and&nbsp\;<a href="http://deeplearning.co m/">deeplearning.com</a>&nbsp\;&nbsp\;&ldquo\;We&rsquo\;ll learn the core principles behind neural networks and deep learning by attacking a concret e problem: the problem of teaching a computer to recognize handwritten dig its. &hellip\;it can be solved pretty well using 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 need to be a professional program mer.&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 attending the series:<br />* Learn the core principles behind neural networks and deep learning.<br />* See a simple Python program that solves a concrete problem: teaching a computer to recognize a handwritten digit.<br />* Improve the result through incor porating more and more core ideas about neural networks and deep learning. <br />* Understand the theory\, with worked-out proofs of fundamental&nbsp \;</p>\n<p>Pre-requisites: There is some heavier mathematics in learning t he four fundamental equations behind backpropagation\, so a basic familiar ity with multivariable calculus and matrix algebra is expected\, but nothi ng advanced is required. (The backpropagation equations can be also just a ccepted without bothering with the proofs since the provided Python code f or the simple network just make use of the equations.) Basic familiarity w ith Python or similar computer language.</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR