October 2017
Beginner to intermediate
270 pages
7h
English
Content preview from Machine Learning for Developers
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The gradient descent loop
So it's time to look for a method to converge to the minimum of a function, knowing only where I am on the surface, and possibly the gradient at the point on the surface I am standing on:
- Start at a random position (remember, we don't know anything about the surface yet)
- Look for the direction of maximum change (as the function is convex, we know it will guide us to the minimum)
- Advance over the error surface in that direction, proportionally to the error amount
- Adjust the starting point of the next step to the new point on the surface where we landed and repeat the process
This method allows us to discover the path for the minimization of our values in an iterative way and in a limited time, when compared to a ...
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