Udemy - Deep Learning for NLP - Part 10 torrent

Deep Learning for NLP - Part 10
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 19 lectures (3h 50m) | Size: 2 GB
Fake News Detection
What you'll learn:
Deep Learning for Natural Language Processing
Fake news detection
Knowledge based fake news detection
Style based fake news detection
Propagation based fake news detection
Credibility based fake news detection
DL for NLP

Requirements
Basics of machine learning
Basic understanding of deep learning models

Description
Fake news is now viewed as one of the greatest threats to cracy, journalism, and freedom of expression. The reach of fake news was best highlighted during the critical months of the 2016 U.S. presidential election campaign. During that period, the top twenty frequently-discussed fake election stories generated 8.7M shares, reactions, and comments on Facebook, ironically, more than the 7.4M for the top twenty most-discussed election stories posted by 19 major news websites. Research has shown that compared to the truth, fake news on Twitter is typically retweeted by many more users and spreads far more rapidly, especially for political news. Our economies are not immune to the spread of fake news either, with fake news being connected to stock market fluctuations and large trades. For example, fake news claiming that Barack Obama, the 44th President of the United States, was injured in an explosion wiped out $130 billion in stock value in 2017. These events and losses have motivated fake news research and sparked the discussion around fake news, as observed by skyrocketing usage of terms such as “post-truth” – selected as the international word of the year by Oxford Dictionaries in 2016.

The many perspectives on what fake news is, what characteristics and nature fake news or those who disseminate it share, and how fake news can be detected motivate the need for a comprehensive introduction and in-depth analysis, which this course aims to develop. This course is divided into three sections.