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SUMMARY:Project-based Python Workshop 3
DESCRIPTION:On Friday\, February 5\, 2021 at 10:00 a.m.\, IEEE Toronto WIE will host a Python workshop\, “How to Use LSTM for Text and Time Series Classification”. \nDay & Time: Friday\, February 5\, 2021\n10:00 a.m. – 12:00 p.m. \nSpeaker(s): Enas Tarawneh \nOrganizer(s): IEEE Toronto WIE\, York University WiCSE \nLocation: Virtual \nContact: Hina Tabassum \nAbstract: \nThis workshop focuses on how to classify or label text using bi-LSTM RNNs. It includes pre-processing/cleaning of the text and handling severely imbalanced classes using SMOTE\, oversampling\, under-sampling\, class count\, and log smoothen weights. Using different types of LSTM such as vanilla LSTM\, and Bi-LSTM\, we focus on time series problems with categorical data.  In summary\, this workshop will cover: \na) Preprocessing text and data\nb) Handling imbalanced datasets\nc) Use different types of LSTMs for text and time series classification\nd) Produce meaningful classification reports \nRegister: Please visit http://bit.ly/39IQFXd to register. \nBiography: Enas Tarawneh is a PhD student at York University in the department of Computer Science and Electrical Engineering. She works in the Vision\, Graphics and Robotics (VGR) Laboratory as a research assistant. Her most recent research involves the development and evaluation of a cloud-based avatar (intelligent agent) for human-robot interaction that is part of a project funded by VISTA. She holds an OGS and VISTA doctoral scholarship.  Prior to this\, Enas worked as an academic Lead\, instructor\, and e-learning coordinator in the Institute of Applied Technology in UAE in which she received an award for “Distinguished Curriculum Support” and another for “Excellence in E-learning coordination”. Most importantly\, Enas is a wife and mother of three\, that believes that open-mindedness and positivism is the best accomplishment and the source of true happiness.
URL:https://www.ieeetoronto.ca/event/project-based-python-workshop-3/
CATEGORIES:Women in Engineering
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