💥 Edge#102: DeepMind Redefines One of the Most Important Algorithms in ML as a Game
That's absolutely fascinating!
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💥 What’s New in AI: DeepMind Wants to Redefine One of the Most Important Algorithms in Machine Learning as a Game
The DeepMind work is one of those papers that you can’t resist reading just based on the title. Redefining PCA sounds ludicrous. And yet, DeepMind’s thesis makes perfect sense the minute you deep dive into it.
Principal component analysis (PCA) is one of the key algorithms that are part of any machine learning curriculum. Initially created in 1901 by Karl Pearson, PCA is a fundamental algorithm for understanding data in high-dimensional spaces, which are common in deep learning problems. More than a century after its invention, PCA is such a key part of modern deep learning frameworks that it’s rarely questioned if there could be a better approach.
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