The machine learning algorithm cheat sheet. Reinforcement learning Reinforcement learning analyzes and optimizes the behavior of an agent based on the feedback from the environment. Machines try different scenarios to discover which actions yield the greatest reward, rather than being told which actions to take. Repo for the Deep Reinforcement Learning Nanodegree program - udacity/deep-reinforcement-learning.
Lately, I spent some time on various data science projects: predictive analysis, natural language processing, graph analysis, etc. Rununblocked games.
Reinforcement Learning Cheat Sheet Examples
Behind the scene, they all share the same machine learning algorithms. Of course, those models barely represent 10 lines of code in my notebooks, thanks to the wonderful open-source libraries accessible today.
But I wanted to go back to the basics and offer a clear picture of how machine learning works, under the hood.
Gr. 1home. The idea with this project is to create a simple, concise, potentially exhaustive document about the most common machine learning algorithms.
A cheatsheet one could come back to for a quick read, in case of doubt or just to keep things clear.
This cheatsheet focus on how algorithms work: the learning, the predictions, the representation or even the expected inputs. I also added a few business oriented usecases, in order to show the usability of those methods.
But coding is not included in the document, as I consider it highly dependent on the chosen language and library. Furnished documentations often constitute a good, comprehensive knowledge base.
Reinforcement Learning Ppt
Deep Learning and Reinforcement Learning methods are also not present here, as they surely require their own cheatsheets.
The Machine Learning Cheatsheet is a 5-pages document that can be found on my github.
Reinforcement Learning Cheat Sheet Printable
This cheatsheet is meant to be a constant work in progress, so please feel free to contact me for any possible improvement!
Reinforcement Learning Cheat Sheet Answer
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