Slopes in many directions at once, and the ball that rolls to the bottom: gradient descent.
Module 4 · 0/7 done
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Going downhill, one step at a time
Here is gradient descent in one dimension. The curve is a loss: how wrong a model is for each value of its parameter w. We start somewhere, look at the slope, and take a step against it — downhill.
w←w−η⋅dwdL
Fig 4.6The anatomy of one step. Drag the point; the green arrow is where one step of gradient descent lands.