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When minimizing the sum of squared errors J(w) = minw Σ1 (ƒ(x¡; w) — yi) for Least Mean Squares we want to update our weights using which of the following: O the integral O the postive gradient O the negative gradient O the closed form equation

When minimizing the sum of squared errors J(w) = minw Σ1 (ƒ(x¡; w) — yi) for Least Mean Squares we want to update our weights using which of the following: O the integral O the postive gradient O the negative gradient O the closed form equation

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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When minimizing the sum of squared errors J(w) = minw Σ1 (ƒ(x;; w) – y;)² for Least Mean Squares we want to update our weights using which of the following: O the integral O the postive gradient O the negative gradient O the closed form equation
Transcribed Image Text:When minimizing the sum of squared errors J(w) = minw Σ1 (ƒ(x;; w) – y;)² for Least Mean Squares we want to update our weights using which of the following: O the integral O the postive gradient O the negative gradient O the closed form equation
Online learning is when we update our model based on all data samples none of the above one data sample at a time a subset of data samples
Transcribed Image Text:Online learning is when we update our model based on all data samples none of the above one data sample at a time a subset of data samples
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