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 in machine learning models
- Model aleatory and epistemic uncertainty using TensorFlow Probability
All the code files for this chapter can be found at https://packt.link/dltfchp12
Let’...
