4. In linear discrimination, gradient descent is used to find the minimum of the error function. With error function defined as the cross entropy E(w, wo X) == − Σ[rt log y² + (1 - rt) log(1 - y¹)] and yt = tanh(w²xt + wo), derive the update for w, where j ‡ 0. Note: Given y = tanh(a), the derivative by = 1 - y². да
4. In linear discrimination, gradient descent is used to find the minimum of the error function. With error function defined as the cross entropy E(w, wo X) == − Σ[rt log y² + (1 - rt) log(1 - y¹)] and yt = tanh(w²xt + wo), derive the update for w, where j ‡ 0. Note: Given y = tanh(a), the derivative by = 1 - y². да
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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please explain all details thanks .
![4.
In linear discrimination, gradient descent is used to find the minimum of the error
function. With error function defined as the cross entropy E(w, wo|X) = − Σ[rt log yt + (1 −
rt) log(1 — y¹)] and yt = tanh(w²xt + wo), derive the update for w; where j ‡ 0.
Note: Given y tanh(a), the derivative
=
Əy
да
= 1- y².](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F4f19521b-984a-46ea-87dd-80ca1e198282%2Fd53e0bd0-b444-41f9-b8d9-46be9bc5c0fa%2F4hotes_processed.png&w=3840&q=75)
Transcribed Image Text:4.
In linear discrimination, gradient descent is used to find the minimum of the error
function. With error function defined as the cross entropy E(w, wo|X) = − Σ[rt log yt + (1 −
rt) log(1 — y¹)] and yt = tanh(w²xt + wo), derive the update for w; where j ‡ 0.
Note: Given y tanh(a), the derivative
=
Əy
да
= 1- y².
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