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 IZ92M/dYpmCIBgEAP9ZUWg for ; Thu, 20 May 2021 14:41:27 +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 C8B88B36D1 for ; Thu, 20 May 2021 14:41:27 +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 sbvffoa-QqmO for ; Thu, 20 May 2021 14:41:24 +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 C78B8B36CE for ; Thu, 20 May 2021 14:41:24 +0200 (CEST) From: Frederic Magniez Content-Type: multipart/alternative; boundary="Apple-Mail=_0A416428-6ABA-4EF4-9E4D-2C6C88DBCCDC" Mime-Version: 1.0 (Mac OS X Mail 14.0 \(3654.80.0.2.43\)) Subject: Fwd: [diip-perimeter] diiP Seminar June 2, 4pm: Deep Learning: An overview using Multi Layer Perceptrons (MLPs) + Hands-On Workshop Message-Id: <26142243-0D47-45DE-A3FC-795C9D5AF783@irif.fr> References: To: lettre@irif.fr Date: Thu, 20 May 2021 14:41:23 +0200 X-Mailer: Apple Mail (2.3654.80.0.2.43) --Apple-Mail=_0A416428-6ABA-4EF4-9E4D-2C6C88DBCCDC 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] diiP Seminar June 2, 4pm: Deep Learning: An = overview using Multi Layer Perceptrons (MLPs) + Hands-On Workshop > Date: 20 May 2021 at 12:14:09 CEST > Reply-To: Ana=C3=AFs De Muret De Labouret = >=20 > Dear all, >=20 > We are glad to announce the next seminar from the Data Intelligence = Institute of Paris (diiP). Please find all the information below. > Deep Learning: An overview using Multi Layer Perceptrons (MLPs) + = Hands-On Workshop > Who: Dr Foula Vagena (Universit=C3=A9 de Paris, diiP) > When: June 2, 4pm (Paris time) > Where: online (zoom)=20 > = https://u-paris.zoom.us/j/89283382013?pwd=3DYzR4Z1ROV3AvcVJibDN5RTVlS0tTdz= 09 = >=20 > Meeting ID: 892 8338 2013 > Passcode: 469904 > title: > An overview using Multi Layer Perceptrons (MLPs) > abstract: > Deep Learning (DL for short) is a field of machine learning that is = concerned with algorithms based on (artificial) neural networks and = representation learning. The quintessential example of a deep learning = model is the feedforward deep network or multilayer perceptron (MLP). In = this tutorial we will provide an overview DL and present its main = components (e.g. tensors, layered composition of models/functions). We = will focus on MLPs and using that we will delineate and explain the main = steps/concepts for DL-based modeling. The tutorial will conclude with an = illustrative hands-on example of MLP-supported regression and = classification models. > The hands-on workshop will focus on MLP supported regression + = classification examples. > 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 de Muret 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=_0A416428-6ABA-4EF4-9E4D-2C6C88DBCCDC 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] = diiP Seminar June 2, 4pm: Deep Learning: An overview using Multi Layer = Perceptrons (MLPs) + Hands-On Workshop
Date: = 20 May 2021 at 12:14:09 CEST
Reply-To: = Ana=C3=AFs De Muret De Labouret = <anais.de-muret-de-labouret@parisdescartes.fr>

Dear all,


We are glad = to announce the next seminar from the Data Intelligence Institute of = Paris (diiP). Please find all the information = below.

Deep Learning: An overview using Multi Layer = Perceptrons (MLPs) + Hands-On Workshop

Who: = Dr Foula Vagena (Universit=C3=A9 de Paris, diiP)
When: = June 2, 4pm (Paris time)
Where: online = (zoom) 
https://u-paris.zoom.us/j/89283382013?pwd=3DYzR4Z1ROV3AvcVJibDN= 5RTVlS0tTdz09

Meeting ID: 892 8338 2013
Passcode: 469904
title:
An overview using Multi = Layer Perceptrons (MLPs)
abstract:
Deep Learning (DL for = short) is a field of machine learning that is concerned with algorithms = based on (artificial) neural networks and representation learning. The = quintessential example of a deep learning model is the feedforward deep = network or multilayer perceptron (MLP). In this tutorial we will provide = an overview DL and present its main components (e.g. tensors, layered = composition of models/functions). We will focus on MLPs and using that = we will delineate and explain the main steps/concepts for DL-based = modeling. The tutorial will conclude with an illustrative hands-on = example of MLP-supported regression and classification models.
The hands-on workshop will = focus on MLP supported regression + classification examples.
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 de Muret 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=_0A416428-6ABA-4EF4-9E4D-2C6C88DBCCDC--