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Besides local minima, saddle points are another reason for gradients to vanish. A saddle point is any location where all gradients of a function vanish but which is neither a global nor a local minimum. Consider the function f(x) = r. Its first and second derivative vanish for r 0. Optimization might stall at this point, even though it is not a minimum. %3D

Besides local minima, saddle points are another reason for gradients to vanish. A saddle point is any location where all gradients of a function vanish but which is neither a global nor a local minimum. Consider the function f(x) = r. Its first and second derivative vanish for r 0. Optimization might stall at this point, even though it is not a minimum. %3D

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Besides local minima, saddle points are another reason for gradients to vanish. A saddle point is any location where all gradients of a function vanish but which is neither a global nor a local minimum. Consider the function f(r) = r³. Its first and second derivative vanish for r = 0. Optimization might stall at this point, even though it is not a minimum.
Transcribed Image Text:Besides local minima, saddle points are another reason for gradients to vanish. A saddle point is any location where all gradients of a function vanish but which is neither a global nor a local minimum. Consider the function f(r) = r³. Its first and second derivative vanish for r = 0. Optimization might stall at this point, even though it is not a minimum.
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