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This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the...

evolution-strategies-starter is an archived OpenAI research project that provides a distributed implementation of the algorithm described in the paper “Evolution Strategies as a Scalable Alternative to Reinforcement Learning” by Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever. The repository demonstrates how to scale Evolution Strategies (ES) for reinforcement learning tasks using a master-worker architecture, where the master node broadcasts parameters to multiple workers, and the workers return performance results after evaluation. This approach allows for efficient parallelization and robustness against worker termination, making it ideal for distributed execution on Amazon EC2 spot instances. The codebase supports building custom AMIs with Packer, integrates with MuJoCo for simulation-based experiments, and includes scripts for launching and managing large-scale runs. ...

...It is meant to be used - by pupils to get as many sheets as they want - by teachers to re-use the produced sheets in their own work. Documentation for users is there: http://mathmaker.readthedocs.io/en/master/user_index.html

ResearchMaster is a tool that you can trust to help with organization, multitasking, and research. ResearchMaster provides a place to capture your fleeting thoughts and ideas, and it allows you to hit-the-ground-running on projects.