Bayesian Deep Learning

Neural Networks in PyMC3 estimated with Variational Inference (c) 2016 by Thomas Wiecki Current trends in Machine Learning There are currently three big trends in machine learning: Probabilistic Programming, Deep Learning and “Big Data”. Inside of PP, a lot of innovation is in making things scale using Variational Inference. In this blog post, I will show how to use Variational Inference in PyMC3 to fit a simple Bayesian Neural Network. I will also discuss how bridging Probabilistic Programming and Deep Learning can open up very interesting avenues to explore in future research. Probabilistic Programming at scale Probabilistic Programming allows very flexible creation of custom probabilistic models and is mainly concerned with insight and learning from your data. The approach is inherently Bayesian so we can specify priors to inform and…


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