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Here's an "awesome" reading list we compiled while studying Software Engineering best practices for Machine Learning applications. Anything we missed? https://github.com/SE-ML/awesome-seml …
#SE4ML@vision_claire@ic4ai@m_learningnews@machinelearnflx@TeachTheMachine@ELLISforEuropepic.twitter.com/9GXlYo7sEM
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Interested in software engineering for AI/ML-enabled systems? After teaching a course on the topic last semester, I finally managed to compile a reading list in the form of an annotated bibliography: https://github.com/ckaestne/seaibib … (PRs welcome)
#SE4ML#SE4AI -
Machine Learning: The High Interest Credit Card of Technical Debt
#SE4ML#NIPS2014 ML specific risk factors, design anti-patterns and their mitigations: hidden feedback loops, undeclared consumers, data dependencies, changes in the external world etchttps://ai.google/research/pubs/pub43146 … -
#MachineLearning : the design patterns to be avoided/refactored where possible.#NIPS2014#SE4ML http://research.google.com/pubs/pub43146.html …
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