2 Matching Annotations
- Jan 2022
-
Local file Local file
-
1.1 Bernoulli distribution
$$ Y \sim f_{B}(y ; \theta)= \begin{cases}\theta^{y}(1-\theta)^{1-y} & \forall y \in\{0,1\} \\ 0 & \text { otherwise }\end{cases} $$
$$E[Y]=\theta$$
$$var(Y)=\theta(1-\theta)$$
-
- Oct 2015
-
cs231n.github.io cs231n.github.io
-
full loss function as coming from a Gaussian prior over the weight matrix WW, where instead of MLE we are performing the Maximum a posteriori (MAP) estimation. We mention these interpretations to help your intuitions, but the full details of this derivation are beyond the scope of this class.
Can anyone provide resources where I can find this derivation? In particular, the derivation for the regularization term \(R(W)\) coming from a Gaussian prior on \(W\).
Tags
Annotators
URL
-