Foula Vagena – Convolutional Neural Networks: An Overview and Applications

2021, diiP, Seminars + Hands-On Workshops

Foula Vagena
July 7, 4pm
online (zoom)

 

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.

 

Dr Foula Vagena
(Université Paris Cité, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université Paris Cité. 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.

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