Series of articles covering probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e. know what they do not know.
We develop our models using TensorFlow and TensorFlow Probability (TFP). TFP is a Python library built on top of TensorFlow. We are going to start with the basic objects that we can find in TensorFlow Probability (TFP) and understand how can we manipulate them. We will increase complexity incrementally over the following weeks and combine our probabilistic models with deep learning on modern hardware (e.g. GPU).