5,321
edits
Machine Learning: Difference between revisions
→Loss functions
No edit summary |
|||
| Line 32: | Line 32: | ||
&= -\sum [(y^{(i)})x^{(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)}]\\ | \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 | &= \sum g(\theta^t x^{(i)})(1-g(\theta^t x^{(i)})) x^{(i)} (x^{(i)})^T \quad | ||
\end{aligned} | \end{aligned} | ||
</math><br> | </math><br> | ||