🚧 Archived 🚧
The blog where these notebooks were repurposed as posts has shut down. The repository will archive the notebooks for everyone to visit. List of notebooks:
- MLE tutorial, where it's shown how to develop the maximum-likelihood estimation algorithm from scratch using TensorFlow Probability.
- MAP tutorial, where it's shown how to build the maximum a posteriori probability estimation algorithm using TensorFlow Probability.
- GLM introduction, it shows an intorduction of generalized linear models with TensorFlow Probability.
- EM tutorial, it explains how to translate the formulas of the expectation-maximization algorithm using TensorFlow.
- Partial regularization, where it's explained how to apply regularization to a subset of variables in a linear model.