In my preceding YouTube videos, we detailed exactly what the gradient of cost is. With that understanding, today we dig into what it means to *descend* this gradient and fit a machine learning model.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
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The Gradient of Quadratic Cost
In this week's video, we derive the Partial Derivatives of Quadratic Cost with respect to the parameters of a simple regression model. This derivation is essential to understanding how machines learn via Gradient Descent.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.