In gradient descent, to minimize the cost function, weights should be updated by subtracting the gradient (DJDW) from the current weights, which moves the optimization process downhill in the cost landscape.
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Stop Guessing: Backpropagation to Code (Part 4)Añadido:
So, how should we change our W's to decrease our cost?
We can now compute DJDW, [music] which tells us which way is uphill in our nine-dimensional optimization space.
If we move this way [music] by adding a scalar times our derivative to all of our weights, our cost will increase.
[music] And if we do the opposite, subtract our gradient from our weights, we will move downhill and reduce our cost.
This simple step downhill is the core of gradient descent >> [music] >> and a key part
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