Published on March 10, 2019 by

TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It’s for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the “layers” module and demonstrate how TFP “distributions” fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty.

See the revamped dev site → https://www.tensorflow.org/

Watch all TensorFlow Dev Summit ’19 sessions → http://bit.ly/TFDS19Sessions
Event homepage → http://bit.ly/TFDS19

Subscribe to the TensorFlow YouTube channel → https://bit.ly/TensorFlow1

Speaker:
Josh Dillon, Software Engineer

Music by Terra Monk → http://bit.ly/TerraMonkTFDS
Event Photo Album → http://bit.ly/TFSummit19

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