ljk628/ML-Systems
papers on scalable and efficient machine learning systems
This collection helps machine learning engineers and researchers stay updated on the latest advancements in designing and implementing fast and scalable machine learning systems. It curates academic papers covering topics like deep learning architectures, optimization techniques, and distributed machine learning. The target user is someone involved in building, deploying, or researching large-scale ML applications.
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Use this if you are a machine learning professional needing to quickly find relevant research papers on making ML models and systems more efficient and performant.
Not ideal if you are looking for introductory materials, practical code examples, or tutorials for applying machine learning concepts.
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Sep 28, 2018
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