We’re on a journey to advance and democratize artificial intelligence through open source and open science. (more…)
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Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks. (more…)
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For many machine learning practitioners, training loop is a universally agreed upon concept as demonstrated by numerous documentations, conference papers to use the word without any reference. It would a helpful concept for many beginners to get familiar ... (more…)
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A broad overview of the sub-field of machine learning interpretability; conceptual frameworks, existing research, and future directions. (more…)
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An ML project ties together large datasets, a slow training process without clear pass/fail acceptance tests, and contains multiple types of deliverables. By comparison, a classical software project just contains code and has a single deliverable (an appl... (more…)
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