January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Optimization
We have to figure out a way to minimize the total loss function and it can be achieved by changing the weight. It can be done by using a crude method like modifying the parameter W over a range of -500 to 500 with a step 0.001. It will help us to find a point where the sum of squares of error becomes 0 or minimum.
But this approach will work out in this scenario because we don't have too many parameters here and computation won't be too challenging. However, when we have a number of parameters, the computation would take a hit.
Here, mathematics comes to our rescue in the form of differentiation (maxima and minima approach) in order to optimize the weights. The derivative of a function at a certain point gives the rate at which ...
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