Gradients in Machine Learning with Jon Krohn
The gradient captures the partial derivative of cost with respect to all of our machine learning model's parameters. To come to grips with it, Jon Krohn carries out a regression on individual data points and derives the partial derivatives of quadratic cost. He then gets into what it means to descend the gradient and derives the partial derivatives of mean squared error, enabling you to learn from batches of data, instead of individual points. He finishes the lesson off with discussions of backpropagation and higher order partial derivatives.
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