Hello,

If I want to uses `Params = Params + inverse(J'*J + mu * I) * g`

instead of `Params = Params + mu * g`

,

where `J`

is a Jacobian matrix and `J'`

is transpose of `J`

, and `J(i,j) = d loss(i) / d params(j)`

,

and `g`

is a gradient vector, `g = J' * loss`

and `I`

is identity matrix,

and `mu`

is a scaler.

How can I compute jacobian matrix and `inverse(J'*J + mu * I) `

, and finally update parameters with the above formulation?

Thanks.