● mastered · kind algorithm · level 4 · 35h
- Requiere: Calculus · Gradient, Jacobian, Hessian
Iteratively step parameters against the loss gradient. SGD, momentum, and Adam are the variants that actually train every model here.
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate function.
Enlaces
- Requiere: Calculus · Gradient, Jacobian, Hessian