Junpeng Lao: Writing effective bayesian programs using TensorFlow and TFP | PyData Córdoba
This tutorial aims to provide some examples of how to write effective Bayesian programs using TensorFlow and Tensorflow Probability. In TFP land, effectiveness usually comes from writing model that could generate batch-able functions, and utilizing modern hardware (GPU, TPU) with compiler accelerator (i.e., XLA). I will give a walkthrough on how to do so and highlight some gotchas. www.pydata.org 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. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...
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