Understanding Lecture 11 Empirical Risk Minimization Part 2
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Key Takeaways about Lecture 11 Empirical Risk Minimization Part 2
- We study a class of iterated
- Gradient descent details: vector-to-scalar functions, empirical risk minimization, convexity
- Subtopic Split(in minutes elapsed) 0-6: Machine learning definition, motivating probabilistic approach to ML, Why Random ...
- This video is
- This video explains the most widely used principle of machine learning:
Detailed Analysis of Lecture 11 Empirical Risk Minimization Part 2
Empirical risk problem so uh given uh this little little loss function l well i mean the uh erm ... minimize this loss with respect to the network parameters this losses the ... touch upon
Close to that then we are sort of optimizing the real thing okay and uh so this is the general principle for
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