NLP 2021 @ BGU.CS
Lecture #04 / Quiz #03
Email *
In machine learning, we are given a dataset of the form {(xi, yi)}, i ∈ [1..N] and aim at learning a function f(x) which maps unseen input feature vectors to ŷ - the predicted value. Distinguish between the 3 types of learning problems by characterizing the mathematical type of the predicted values ŷ:
Given a training dataset D = {(xi, yi)}, i ∈ [1..N], we want to identify a function fΘ() such that the predictions ŷ = fΘ(x) over the training dataset are as accurate as possible. Given a Loss Function L(y,ŷ) - write the criterion that the optimal value of Θ must satisfy: Find θ such that:
Write the expression of the cross-entropy loss which is useful when the predicted output of the model we learn is interpreted as a discrete distribution p(yc|x) for c ∈ [1..C] (C-way classification model). f(x) = ŷ = (ŷ1 ... ŷC) is a distribution over the C possible classes.
Submit
Clear form
Never submit passwords through Google Forms.
This content is neither created nor endorsed by Google. Report Abuse - Terms of Service - Privacy Policy
Google Forms