BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Sabre//Sabre VObject 4.5.8//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Europe/Zurich
X-LIC-LOCATION:Europe/Zurich
TZURL:http://tzurl.org/zoneinfo/Europe/Zurich
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19810329T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19961027T030000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:news839@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20190319T144242
DTSTART;TZID=Europe/Zurich:20190320T110000
SUMMARY:Seminar in probability theory: David Belius (Universität Basel)
DESCRIPTION:This is the first talk in a five part series of talks on deep l
 earning from a theoretical point of view\, held jointly between the probab
 ility theory and machine learning groups of the Department of Mathematics 
 and Computer Science. The four invited speakers that follow after this tal
 k are young researchers who are contributing in different ways to what wil
 l hopefully eventually be a comprehensive theory of deep neural networks.I
 n this first talk I will introduce the main theoretical questions about de
 ep neural networks:1. Representation - what can deep neural networks repr
 esent?2. Optimization - why and under what circumstances can we successfu
 lly train neural networks?3. Generalization - why do deep neural networks
  often generalize well\, despite huge capacity?As a preface I will review 
 the basic models and algorithms (Neural Networks\, (stochastic) gradient d
 escent\, ...) and some important concepts from machine learning (capacity\
 , overfitting/underfitting\, generalization\, ...).
X-ALT-DESC:This is the first talk in a five part series of talks on deep le
 arning from a theoretical point of view\, held jointly between the probabi
 lity theory and machine learning groups of the Department of Mathematics a
 nd Computer Science. The four invited speakers that follow after this talk
  are young researchers who are contributing in different ways to what will
  hopefully eventually be a comprehensive theory of deep neural networks.<b
 r /><br />In this first talk I will introduce the main theoretical questio
 ns about deep neural networks:<br />1. Representation&nbsp\;- what can dee
 p neural networks represent?<br />2. Optimization&nbsp\;- why and under wh
 at circumstances can we successfully train neural networks?<br />3. Genera
 lization&nbsp\;- why do deep neural networks often generalize well\, despi
 te huge capacity?<br /><br />As a preface I will review the basic models a
 nd algorithms (Neural Networks\, (stochastic) gradient descent\, ...) and 
 some important concepts from machine learning (capacity\, overfitting/unde
 rfitting\, generalization\, ...). 
END:VEVENT
END:VCALENDAR