Assertion: gradient descent | /r/HasProperty | way
Facets:
value: by updating the parameters
type: MANNER
value: in conclusion
type: MANNER
Source sentences:
text: Gradient descent is a way to minimize an objective function \(J(\theta)\) parameterized by a model's parameters \(\theta
text: In conclusion, gradient descent is a way for us to calculate the best set of values for the parameters of concern.
text: Gradient descent is a way to minimize an objective function J(θ) parameterized by a multivariate model's parameter θ parameters by updating the parameters in the opposite direction of the gradient of the objective function ∇J(θ) w.r.t.