In neural network training, the optimal weights (W_check) are found by minimizing the loss function, which measures the 'badness' or error of the model's predictions; the goal is to select weights that result in the least cost for the given data, with larger weight values causing the loss curve to decrease more rapidly toward zero.
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Lesson 06: A softer perceptron, part II: likelihood and loss #education #science #deeplearningAdded:
Overall, what was our objective here?
This guy here is telling me the badness of my weight. Which kind of weight would we want for our model?
The least bad weights, right? Finding the optimal weight.
It's going to be argmin of this guy, right?
Which we call W check. So, [clears throat] W check is the arg minimizer of our loss. How do I pay the least amount of cost for the red guys?
Which of these 1 2 3 4 5 curves should I pick? Purple, right? The purple has the least amount of cost for my red guys.
The largest my W1 and the sooner this purple will go down to zero at the after the crossing point, right?
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