StackedML
Practice
Labs
Questions
Models
Pricing
Sign in
Questions
/
Math Foundations
/
Calculus
/
Partial derivatives
← Previous
Next →
578.
Partial Derivative of Loss
easy
In ML, the loss function L(w₁, w₂, ..., wₙ) depends on many weights. What does ∂L/∂wᵢ tell us?
A
How much the total gradient magnitude changes when wᵢ is removed from the model during the optimization process
B
How much the loss changes per unit increase in weight wᵢ with all other weights held fixed
C
How much the loss changes per unit increase in all weights simultaneously
D
How much the loss changes when all weights are scaled proportionally by wᵢ
Sign in to verify your answer
← Back to Questions