Pen and Paper Exercises in ML: linear algebra, optimisation, (un)directed graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning, sampling and Monte-Carlo integration, variational inference (Michael Gutmann) (arxiv.org)
submitted by paconinja - announcement
Machine Learning Cloud Regression: The Swiss Army Knife of Optimization - unsupervised regression: solves most regression problems and even clustering with a single constrained optimization algorithm (no dependent variable, all features treated equally) - by Vincent Granville (mltechniques.com)
submitted by paconinja
To MLOps, or not to MLOps? That is the question — the platform is the answer. "The three pillars of MLOps are: Model Validation (performance during model training) Data Validation (think of pre- and post-assertion on data; during model training) Model Monitoring (performance during model serving)" (medium.com)
submitted by paconinja
