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Machine Learning: Difference between revisions
→Cross Entropy
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;Notes | ;Notes | ||
* If our model is <math>g(\theta^Tx^{(i)})</math> where <math>g(x)</math> is the sigmoid function <math>\frac{e^x}{1+e^x}</math> then this is convex | * If our model is <math>g(\theta^Tx^{(i)})</math> where <math>g(x)</math> is the sigmoid function <math>\frac{e^x}{1+e^x}</math> then this is convex | ||
{{hidden | Proof | | |||
<!--{{hidden | Proof | | |||
We show that the Hessian is positive semi definite.<br> | We show that the Hessian is positive semi definite.<br> | ||
<math> | <math> | ||
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which is a PSD matrix | which is a PSD matrix | ||
}} | }} | ||
--> | |||
===Hinge Loss=== | ===Hinge Loss=== | ||