Bayesian probabilistic techniques allow machine learning practitioners to encode expert knowledge in otherwise-uninformed models and support uncertainty in model output. Probabilistic deep learning models take this further by fitting distributions rather than point estimates to each of the weights in a neural network, allowing its builder to inspect the prediction stability for any given set of input data. Following a slew of recent technical advancements, it's never been easier to apply probabilistic modeling in a deep learning context, and TensorFlow Probability offers full support for probabilistic layers as a first-class citizen in the TensorFlow 2.0 ecosystem. This video tutorial will focus on the motivation for probabilistic deep learning and the trade-offs and design decisions relevant to applying it in practice, with applications and examples demonstrated in TensorFlow Probability.

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