Probabilistic Programming Using TensorFlow Probability

Probabilistic Programming Using TensorFlow Probability

Probabilistic Programming Using TensorFlow Probability. This talk will teach you when, why and how to use TensorFlow probability. 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, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions.

What is Probabilistic Programming

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, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions.

Probabilistic programming allows us to encode domain knowledge to understand data and make predictions. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and Deep Learning. With TensorFlow 2.0, TFP can be very easily integrated into your code with very few changes and the best part - it even works with tf.keras!

This talk will teach you when, why and how to use TensorFlow probability.

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