# Follow the regularized leader 1. [FTRL the paper](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf) by McMahan et al. 2. [FTRL](https://www.quora.com/What-is-an-intuitive-explanation-of-Follow-the-Regularized-Leader-FTRL-algorithm) by nicolo compolongo - "The “Follow the Regularized Leader” algorithm stems from the online learning setting, where the learning process is sequential. In this setting, an online player makes a decision in every round and suffers a loss." 3. [A thorough medium article](https://medium.com/@dhirajreddy13/factorization-machines-and-follow-the-regression-leader-for-dummies-7657652dce69) by dhiraj reddy 4. [Keras on FTRL](https://keras.io/api/optimizers/ftrl/) - ""Follow The Regularized Leader" (FTRL) is an optimization algorithm developed at Google for click-through rate prediction in the early 2010s. It is most suitable for shallow models with large and sparse feature spaces. The algorithm is described by [McMahan et al., 2013](https://research.google.com/pubs/archive/41159.pdf). The Keras version has support for both online L2 regularization (the L2 regularization described in the paper above) and shrinkage-type L2 regularization (which is the addition of an L2 penalty to the loss function)." --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.mlcompendium.com/business-domains/follow-the-regularized-leader.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.