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

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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[@WahranRai]

6:38 The norm function is a standardisation function, normalisation (scaling) is transforming into [0,1], [-1,1] etc

[@faatemehch96]

that was really helpful, thank you

[@youkorii]

1. Before training the network it must return the prior distribution of the outputs for any input, am I wrong?
2. If we have (1) how to show that the training procedure is using Bayes Rule to update the priors, I mean replacing X by X|y?

[@yzarc314]

Hi! Who is the speaker?

[@user-or7ji5hv8y]

Is there a GitHub?

[@cordeirojoao]

Hello,
Great video, but i believe its not complete.
Can you please let me know whats the link the rest of the video?
Thank you :)

[@OpenDataScienceCon]

Hi Joao,
thank you for the question - here is our learning resource where you can find this video and a lot more materials on the topic of AI and Data Science - learnai.odsc.com

[@rt-odsc8270]

@@OpenDataScienceCon do you guys like to share full video details please.

[@ZsoltSafrany]

@@OpenDataScienceCon you mean the "ODSC Europe 2019" one for $400? Very Open indeed. xD