Antisocial Online Behavior Detection using Deep Learning

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Antisocial Online Behavior Detection using Deep Learning

The is the overview of the research paper Antisocial Online Behavior Detection using Deep Learning 2020

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Courselet Content

2 components

Requirements

  • Interest in Cyberbullying Detection and Machine Learning

General Overview

Description

The course is dedicated to the topic of identification cyberbullying using deep learning. It is based on the research paper Antisocial Online Behavior Detection using Deep Learning by Zinovyeva et al. 2020

 

*Work not related to Amazon 

 

The code is available in Quantlet org at Github and is searchable through Quantlet

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Meet the instructors !

instructor
About the Instructor

To upload my phd presentations

instructor
About the Instructor

Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn.  He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”.  He directs  IDA Institute for Digital Assets,  

  University of Economic Studies, Bucharest, RO. His research focuses on data analytics, dimension reduction and quantitative finance.  He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the www.quantlet.com platform, a cryptocurrency index, CRIX www.royalton-crix.com  He is 玉山学者 (Yushan Scholar), web page hu.berlin/wkh