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now that's clear..
lovelplants 1 month ago
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Makes my head spin.
grunder20 1 month ago
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fantastic lecture!!
grunder20 2 months ago
Take his online courses, very interesting stuff.
SkitchThat 2 months ago
now i know why should i learn math.
2osiris1503 5 months ago
very good explanation of SVM thanks
albakrim 1 year ago
his worst lecture so far. There is no sequence and he always say we will come back to it later and moves on.
You must consult his lecture notes too!
kapildalwani 1 year ago
this is phat
timmurfy 1 year ago
GREAT series. I've been looking around for a *long* time for something to explain ML this clearly to me... Granted digging around gave me some necessary background - but still a definite jump in clarity over just reading Elements, etc...
Thank you Stanford and Dr Ng!
neverquitclimbing 1 year ago 2
Soft Margin Support Vector Machines starts at time 38
Lecture includes Karush-Kuhn-Tucker conditions
PatriciaHoffmanPhD 1 year ago 2
Very good lectures, especially for statisticians
knowledgekurd 1 year ago 3
Comment removed
halojetter 2 years ago
one sinlge question. @33:00. can someon tell me why THETA_p(w) = maxL(w,a,b) = f(w). thanks
kourdoun85 2 years ago
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theta_p(w) = max L(w,a,b)=f(w) since some w satisfies the constraints, so h(w) will be zero and we can set alpha = 0 so as to maximize L(w,a,b)
halojetter 2 years ago 2
excellent work!
1888junkteam 2 years ago
Great lecture! Thanks for uploading..
roywwcheng 2 years ago
it's very nice lecture. I really appreciate it
daywednes 2 years ago 4
You have the most atrocious notation I've ever seen.. and I say this as someone who loves these lectures. Please talk to someone in math or physics. They've been refining notation for centuries now.
7errain 2 years ago
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BORING
bellagril5 3 years ago