I have been trying to use the scikit-learn library to solve this problem. Roughly: from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression # Make or ...

I had asked this question earlier, and wanted to continue with a follow-up since I tried some other things and they didn't quite work out. I am essentially trying to optimize an NLP type problem i...

Using CUDA, I would like to solve a system of equations with a non-linear least squares solver. These methods are discussed in an excellent booklet that can be downloaded here. The Jacobian matrix...

While using scipy.optimize.minimize with the trust-constr method I got this UserWarning: scipy\optimize\_hessian_update_strategy.py:187: UserWarning: delta_grad == 0.0. Check if the approximated ...

Can anyone explain to me in a easy and less mathematical way what is a Hessian and how does it work in practice when optimizing the learning process for a neural network ?

I am wondering what information does an Hessian Matrix of an image provides? Does it provide the information of the stable points? What is Hessian matrix used for?

I am currently working in SIFT, I had generated the difference of Gaussian and the extrema image layers. Can anyone explain to me how to use Hessian matrix to eliminate the low contrast keypoint?

I am attempting to implement automatic differentiation for a Python statistics package (the problem formulation is similar to optimization problem formulations). The computational graph is genera...