12
Probabilistic TensorFlow
Uncertainty is a fact of life; whether you are doing a classification task or a regression task, it is important to know how confident your model is in its prediction. Till now, we have covered the traditional deep learning models, and while they are great at many tasks, they are not able to handle uncertainty. Instead, they are deterministic in nature. In this chapter, you will learn how to leverage TensorFlow Probability to build models that can handle uncertainty, specifically probabilistic deep learning models and Bayesian networks. The chapter will include:
- TensorFlow Probability
- Distributions, events, and shapes in TensorFlow Probability
- Bayesian networks using TensorFlow Probability
- Understand uncertainty ...
Get Deep Learning with TensorFlow and Keras - Third Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
