- Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Gchxyg

Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/

To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.e...
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Gchxyg

Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/

To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html

#Stanford #Stanford Online #Stanford Graduate lecture #CS229 #AI #Andrew Ng

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It helps me to think of Laplace smoothing asPr(observation gets label) = (count of observations with label)/(number of observations) -->Pr(observation gets label) = (count of observations with label + 1)/(number of observations + number of possible labels) - Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

It helps me to think of Laplace smoothing asPr(observation gets label) = (count of observations with label)/(number of observations) -->Pr(observation gets label) = (count of observations with label + 1)/(number of observations + number of possible labels)

Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
2020年04月18日　
00:32:07 - 01:20:57
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