Deep neural networks in social media content analysis - Adam Bielski

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How can we use the constantly growing number of photos and videos posted on social media? In this talk I will present three practical examples of deep neural networks applications to multimedia information extraction: logo detection, text extraction and popularity prediction.

Every day large numbers of photos and videos are posted in social media. With the advent of modern deep learning, it is now possible to automatically analyze this content to get more in-depth insights.

In this talk I will present three hands-on examples of how deep neural networks can be applied for social media content analysis. First, I'll present our neural network architecture used to detect logotypes in the videos given a limited amount of training data. Then I will show a working example of text-in-the-wild extraction (detection and recognition) pipeline. Last but not least, I'll show how video thumbnails can be used to predict video popularity.


PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
PyData Warsaw 2017

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