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 # Various machine learning algorithms. Mostly vanilla.
 
+* [bayesian generalized additive models](https://www.ritchievink.com/blog/2018/10/09/build-facebooks-prophet-in-pymc3-bayesian-time-series-analyis-with-generalized-additive-models/)
+* [gradient boosting machines](https://www.ritchievink.com/blog/2018/10/09/algorithm-breakdown-why-do-we-call-it-gradient-boosting/)
 * [dirichlet gaussian mixture models](https://www.ritchievink.com/blog/2018/06/05/clustering-data-with-dirichlet-mixtures-in-edward-and-pymc3/)
 * [affinity propagation](https://www.ritchievink.com/blog/2018/05/18/algorithm-breakdown-affinity-propagation/)
 * [genetic algorithms](https://www.ritchievink.com/blog/2018/01/14/computer-build-me-a-bridge/)
 * [support vector machine](https://www.ritchievink.com/blog/2017/11/27/implementing-a-support-vector-machine-in-scala/)
 * [neural network](https://ritchievink.com/blog/2017/07/10/programming-a-neural-network-from-scratch/)
-* [arima models](https://www.ritchievink.com/blog/2018/09/26/algorithm-breakdown-ar-ma-and-arima-models./)
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+* [arima models](https://www.ritchievink.com/blog/2018/09/26/algorithm-breakdown-ar-ma-and-arima-models./)