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Eric J. Ma - An Attempt At Demystifying Bayesian Deep Learning
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PyData New York City 2017
In this talk, I aim to do two things: demystify deep learning as essentially matrix multiplications with weights learned by gradient descent, and demystify Bayesian deep learning as placing priors on weights. I will then provide PyMC3 and Theano code to illustrate how to construct Bayesian deep nets and visualize uncertainty in their results. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
In this talk, I aim to do two things: demystify deep learning as essentially matrix multiplications with weights learned by gradient descent, and demystify Bayesian deep learning as placing priors on weights. I will then provide PyMC3 and Theano code to illustrate how to construct Bayesian deep nets and visualize uncertainty in their results. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
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