Meeting su Data Science e Machine Learning

28 Febbraio

ore 19:00 – 20:30

Sede Musixmatch

Via San Vitale, 6 Bologna

Data Science Bologna organizza, con la gentile concessione degli spazi da parte di Musixmatch, il secondo evento della serie “Meeting su Data Science e Machine Learning”. Avremo il piacere di ascoltare un brillante early-stage researcher in Machine Learning del dipartimento di Scienze Informatiche di Bologna: Vincenzo Lomonaco

Sei un professionista (o aspirante) data scientist? Sei un appassionato di machine learning? Sei un ricercatore o uno studente del settore? Sei semplicemente curioso di come le nuove tecnologie e l’intelligenza artificiale stiano  plasmando il nostro futuro e il nostro modo di vivere?

Ti aspettiamo!

La partecipazione all’evento è libera, è gradita l’adesione al link (solo altri 10 posti disponibili):

https://www.eventbrite.it/e/biglietti-meeting-su-data-science-e-machine-learning-56848956760

Al termine della presentazione ci sarà ampio spazio per le domande, il netwoking e l’organizzazione del prossimo evento.

L’evento è sponsorizzato da 3bee.

Informazioni sullo speaker

Vincenzo Lomonaco

Short bio: A 3rd year PhD student at the University of Bologna, Italy and founder of ContinualAI.org, a non-profit research organization on Continual/Lifelong Learning for AI. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. He has been a visiting scholar at the Purdue University, USA in 2017 and at the ENSTA ParisTech Grande École, France in 2018. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks”. His main research interests include continual/lifelong Learning with deep architectures, multi-task learning, knowledge distillation and transfer, and their applications to embedded systems, robotics and internet-of-things.

Titolo contributo: Open-Source Frameworks for Deep Learning: an Overview

Abstract: The rise of deep learning over the last decade has led to profound changes in the landscape of the machine learning software development stack both for research and production. In this talk we will provide a comprehensive overview of the open-source deep learning frameworks landscape with both a theoreticaland hands-on approach. After a brief introduction and historical contextualization, we will highlight common features and distinctions of their recent developments. Finally, we will take at deeper look into three of the most used deep learning frameworks today: Caffe, Tensorflow, PyTorch; with practical examples and considerations worth reckoning in the choice of such libraries.

 

 

Categorie: Eventi

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