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"w -= gradient(w) * lr\n",
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"```\n",
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"\n",
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"This is known as *stepping* your parameters, using an *optimizer step*. Notice how we _subtract_ the `gradient * lr` from the parameter to update it. This allows us to adjust the parameter in the direction of the slope by increasing the parameter when the slope is negative and decreasing the parameter when the slope is positive. We want to adjust our parameters in the direction of the slope because our goal in deep learning is to _minimize_ the loss.\n",
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"This is known as *stepping* your parameters, using an *optimizer step*. Notice how we _subtract_ the `gradient * lr` from the parameter to update it. This allows us to adjust the parameter in the opposite direction of the slope by increasing the parameter when the slope is negative and decreasing the parameter when the slope is positive. We want to adjust our parameters in the direction of the slope because our goal in deep learning is to _minimize_ the loss.\n",
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"\n",
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"If you pick a learning rate that's too low, it can mean having to do a lot of steps. <<descent_small>> illustrates that."
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]
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