Project-based Python Workshop 3
This 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:
a) Preprocessing text and data
b) Handling imbalanced datasets
c) Use different types of LSTMs for text and time series classification
d) Produce meaningful classification reports
Date and Time
Location
Hosts
Registration
- Date: 05 Feb 2021
- Time: 10:00 AM to 12:00 PM
- All times are (GMT-05:00) Canada/Eastern
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Speakers
Enas Tarawneh
Biography:
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.