Machine Learning: Difference between revisions

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Machine Learning
Machine Learning
Machine Learning


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so the hessian is positive semi-definite
so the hessian is positive semi-definite
}}
}}
===Cross Entropy===
The cross entropy loss is
* <math>J(\theta) = \sum [(y^{(i)})\log(h_\theta(x)) + (1-y^{(i)})\log(1-h_\theta(x))]</math>
;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
{{hidden | Proof |
<math>
\begin{aligned}
\nabla_\theta J(\theta) &= -\nabla_\theta \sum [(y^{(i)})\log(g(\theta^t x^{(i)})) + (1-y^{(i)})\log(1-g(\theta^t x^{(i)}))]\\
&= -\sum [(y^{(i)})\frac{g(\theta^t x^{(i)})(1-g(\theta^t x^{(i)}))}{g(\theta^t x^{(i)})}x^{(i)} + (1-y^{(i)})\frac{-g(\theta^t x^{(i)})(1-g(\theta^t x^{(i)}))}{1-g(\theta^t x^{(i)})}x^{(i)}]\\
&= -\sum [(y^{(i)})(1-g(\theta^t x^{(i)}))x^{(i)} - (1-y^{(i)})g(\theta^t x^{(i)})x^{(i)}]\\
&= -\sum [(y^{(i)})x^{(i)} -(y^{(i)}) g(\theta^t x^{(i)}))x^{(i)} - g(\theta^t x^{(i)})x^{(i)} + y^{(i)}g(\theta^t x^{(i)})x^{(i)}]\\
&= -\sum [(y^{(i)})x^{(i)} - g(\theta^t x^{(i)})x^{(i)}]\\
\implies \nabla^2_\theta J(\theta) &= \nabla_{\theta} -\sum [(y^{(i)})x^{(i)} - g(\theta^t x^{(i)})x^{(i)}]\\
&= \sum [(g(\theta^t x^{(i)}))(1-g(\theta^t x^{(i)})) x^{(i)} (x^{(i)})^T]
\end{aligned}
</math><br>
which is a PSD matrix
}}
===Hinge Loss===


==Optimization==
==Optimization==