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Probabilistic Deep Learning in TensorFlow: The Why and How | ODSC Europe 2019

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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 model

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