gradient descent method造句

例句与造句

1. The minimization of "'P2 "'is done through a simple gradient descent method.
2. By contrast, gradient descent methods can move in any direction that the ridge or alley may ascend or descend.
3. In order to find the correct value of \ mathbf { w }, we can use gradient descent method.
4. Like the projection pursuit situation, we can use gradient descent method to find the optimal solution of the unmixing matrix.
5. This function displays a similar convergence rate to the hinge loss function, and since it is continuous, gradient descent methods can be utilized.
6. It's difficult to find gradient descent method in a sentence. 用gradient descent method造句挺难的
7. Consequently, the hinge loss function cannot be used with gradient descent methods or stochastic gradient descent methods which rely on differentiability over the entire domain.
8. Consequently, the hinge loss function cannot be used with gradient descent methods or stochastic gradient descent methods which rely on differentiability over the entire domain.
9. This function is not naturally represented as a product of the true label and the predicted value, but is convex and can be minimized using stochastic gradient descent methods.
10. :Where the computer can really do well is when there is already a reasonable solution-at that point, the computer can use Gradient descent methods to improve the solution.
11. Then in the gradient descent method the values of a _ j, \ mu _ j, W that minimize H [ \ phi ^ * ] can be found as a stable fixed point of the following dynamic system:
12. This important special case has also given rise to many other iterative methods ( or adaptive filters ), such as the least mean squares filter and recursive least squares filter, that directly solves the original MSE optimization problem using gradient descent methods.
13. As the condition number increases, the ease with which the solution vector can be found using gradient descent methods such as the conjugate gradient method decreases, as A becomes closer to a matrix which cannot be inverted and the solution vector becomes less stable.
14. At each iteration, hill climbing will adjust a single element in \ mathbf { x } and determine whether the change improves the value of f ( \ mathbf { x } ) . ( Note that this differs from gradient descent methods, which adjust all of the values in \ mathbf { x } at each iteration according to the gradient of the hill . ) With hill climbing, any change that improves f ( \ mathbf { x } ) is accepted, and the process continues until no change can be found to improve the value of f ( \ mathbf { x } ).