Return-Path: Delivered-To: cadet@irif.fr Received: from mailhub.math.univ-paris-diderot.fr ([81.194.30.253]) by mailhost.irif.fr (Dovecot) with LMTP id Aa6TNohD02A8QAAAP9ZUWg for ; Wed, 23 Jun 2021 16:22:00 +0200 Received: from mailhub.math.univ-paris-diderot.fr (localhost [127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTP id BC32BB2808 for ; Wed, 23 Jun 2021 16:22:00 +0200 (CEST) X-Virus-Scanned: amavisd-new at math.univ-paris-diderot.fr X-Spam-Flag: NO X-Spam-Score: -2.898 X-Spam-Level: X-Spam-Status: No, score=-2.898 tagged_above=-10000 required=5 tests=[ALL_TRUSTED=-1, BAYES_00=-1.9, HTML_MESSAGE=0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mailhub.math.univ-paris-diderot.fr ([127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (mailhub.math.univ-paris-diderot.fr [127.0.0.1]) (amavisd-new, port 10023) with ESMTP id CLNp1wD-eZsY for ; Wed, 23 Jun 2021 16:21:58 +0200 (CEST) Received: from smtpclient.apple (unknown [172.23.37.175]) (Authenticated sender: magniez) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTPSA id E556DB2802 for ; Wed, 23 Jun 2021 16:21:57 +0200 (CEST) From: Frederic Magniez Content-Type: multipart/alternative; boundary="Apple-Mail=_746A515D-68CD-452C-8664-826FAF271378" Mime-Version: 1.0 (Mac OS X Mail 14.0 \(3654.100.0.2.22\)) Subject: =?utf-8?Q?Fwd=3A_=5Bdiip-perimeter=5D_s=C3=A9minaire_diiP=3A_Conv?= =?utf-8?Q?olutional_Neural_Networks=3A_An_Overview_and_Applications_+_Han?= =?utf-8?Q?ds-On_Workshop?= Message-Id: References: <283e0b3532e64c1ba57a9a0a9af6493c@parisdescartes.fr> To: lettre@irif.fr Date: Wed, 23 Jun 2021 16:21:57 +0200 X-Mailer: Apple Mail (2.3654.100.0.2.22) --Apple-Mail=_746A515D-68CD-452C-8664-826FAF271378 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 Fr=C3=A9d=C3=A9ric > Begin forwarded message: >=20 > From: Ana=C3=AFs De Muret De Labouret = > Subject: [diip-perimeter] s=C3=A9minaire diiP: Convolutional Neural = Networks: An Overview and Applications + Hands-On Workshop > Date: 18 June 2021 at 16:12:13 CEST > To: "diip-perimeter@math-info.univ-paris5.fr" = > Cc: "diip@math-info.univ-paris5.fr" > Reply-To: Ana=C3=AFs De Muret De Labouret = >=20 > diiP Seminar + Hands-On = Workshop: > Convolutional Neural Networks: An Overview and Applications > Who: Dr Foula Vagena (Universit=C3=A9 de Paris, diiP) > When: July 7, 4pm (Paris time) > Where: online: = https://u-paris.zoom.us/j/86505371055?pwd=3DRjdVTDAxS1l0eWh1YWpHOEFvRXRZUT= 09 = > title: > Convolutional Neural Networks: An Overview and Applications + Hands-on = workshop > abstract: > Convolutional neural networks (CNNs), are a specialized kind of neural = network for processing data that has a known, grid-like topology. = Examples include time-series data, which can be thought of as a 1D grid = taking samples at regular time intervals, and image data, which can be = thought of as a 2D grid of pixels. Such networks have been tremendously = successful in practical applications. They employ a mathematical = operation called convolution, a specialized kind of linear operation. In = this tutorial we will first describe the convolutional operation and = explain how this is leveraged to form CNN architectures. We will then = describe applications where CNNs have been very succesful and provide a = summary of well known CNN architectures. The tutorial will conclude with = an illustrative hands-on example of a CNN-supported image classification = task. > The Hands-On Workshop will focus on CNN supported image = classification. > short bio: > Zografoula Vagena is a research associate at the Data Intelligence = Institute of Paris (diiP) and affiliated with the Universit=C3=A9 de = Paris. She has been a data science researcher and practitioner for over = ten years. She has worked on different analytics problems including = forecasting, image processing, graph analytics, multidimensional data = analysis, text processing, recommendation systems, sequential data = analysis and optimization within various fields such as transportation, = healthcare, retail, finance/insurance and accounting. She has also = performed research in the intersection of data management and analytics, = and was a primary contributor of the MCDB/SimSQL systems that blended = data management with Bayesian statistics. She holds a PhD in data = management from the University of California, Riverside. > logistics: > Pour recevoir les annonces du diiP, vous pouvez vous inscrire =C3=A0 = notre mailing list en envoyant un mail =C3=A0 = diip@math-info.univ-paris5.fr = avec "Subscribe" dans l'objet du mail. >=20 > Bien =C3=A0 vous, > Ana=C3=AFs pour le diiP >=20 >=20 > Ana=C3=AFs de Muret > Manager de projets, Cit=C3=A9 du Genre = et Data Intelligence Institute of Paris = > Universit=C3=A9 de Paris >=20 > 5, rue Thomas Mann > B=C3=A2timent C des Grands Moulins - Bureau 879C > 75013 Paris >=20 > 01 57 27 52 36=20 --Apple-Mail=_746A515D-68CD-452C-8664-826FAF271378 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8
Fr=C3=A9d=C3=A9ric

Begin forwarded message:

From: = Ana=C3=AFs De Muret De Labouret = <anais.de-muret-de-labouret@parisdescartes.fr>
Subject: = [diip-perimeter] = s=C3=A9minaire diiP: Convolutional Neural Networks: An Overview and = Applications + Hands-On Workshop
Date: = 18 June 2021 at 16:12:13 = CEST
To: = "diip-perimeter@math-info.univ-paris5.fr" <diip-perimeter@math-info.univ-paris5.fr>
Cc: = "diip@math-info.univ-paris5.fr" <diip@math-info.univ-paris5.fr>
Reply-To: = Ana=C3=AFs De Muret De Labouret = <anais.de-muret-de-labouret@parisdescartes.fr>

diiP Seminar + Hands-On = Workshop:
Convolutional Neural Networks: An Overview and = Applications
Who: Dr Foula = Vagena (Universit=C3=A9 de Paris, diiP)
When: July 7, 4pm (Paris time)
Where: online: https://u-paris.zoom.us/j/86505371055?pwd=3DRjdVTDAxS1l0eWh1YWp= HOEFvRXRZUT09

title:
Convolutional Neural Networks: An = Overview and Applications + Hands-on workshop
abstract:
Convolutional neural networks = (CNNs), are a specialized kind of neural network for processing data = that has a known, grid-like topology. Examples include time-series data, = which can be thought of as a 1D grid taking samples at regular time = intervals, and image data, which can be thought of as a 2D grid of = pixels. Such networks have been tremendously successful in practical = applications. They employ a mathematical operation called convolution, a = specialized kind of linear operation. In this tutorial we will first = describe the convolutional operation and explain how this is leveraged = to form CNN architectures. We will then describe applications where CNNs = have been very succesful and provide a summary of well known CNN = architectures. The tutorial will conclude with an illustrative hands-on = example of a CNN-supported image classification task.
The Hands-On Workshop will focus on CNN supported image = classification.
short bio:
Zografoula Vagena is a research associate at the Data = Intelligence Institute of Paris (diiP) and affiliated with the = Universit=C3=A9 de Paris. She has been a data science researcher and = practitioner for over ten years. She has worked on different analytics = problems including forecasting, image processing, graph analytics, = multidimensional data analysis, text processing, recommendation systems, = sequential data analysis and optimization within various fields such as = transportation, healthcare, retail, finance/insurance and accounting. = She has also performed research in the intersection of data management = and analytics, and was a primary contributor of the MCDB/SimSQL systems = that blended data management with Bayesian statistics. She holds a PhD = in data management from the University of California, = Riverside.
logistics:
Pour recevoir les annonces du diiP, vous pouvez vous = inscrire =C3=A0 notre mailing list en envoyant un mail =C3=A0 diip@math-info.univ-paris5.fr avec "Subscribe" dans = l'objet du mail.

Bien = =C3=A0 vous,
Ana=C3= =AFs pour le diiP


Ana=C3=AFs de Muret
Manager de projets, Cit=C3=A9 du Genre et Data= Intelligence Institute of Paris
Universit=C3=A9 de Paris

5, rue Thomas Mann
B=C3=A2timent C des Grands Moulins = - Bureau 879C
75013 Paris

01 57 27 = 52 36 

= --Apple-Mail=_746A515D-68CD-452C-8664-826FAF271378--