- I am trying to apply great features
`Flux`

for likelihood minimization problem.

Using ML language, losses for all training data points are summed up,

there is just a function`LLH()`

to be minimized. Is there an easy hack to`train!`

functions that can help me?

```
using Flux
using Flux.Tracker
#
const data = [randn() for _ in 1:1000]
μ = param(0.5)
σ = param(1.1)
model(x) = 1/(sqrt(2π)*σ)*exp(-(x-μ)^2/(2*σ^2))
LLH() = sum(-log.(model.(data)))
# grad also works
gs = Tracker.gradient(LLH, Params([μ,σ]))
gs[μ], gs[σ]
# now I need to have an infinite training loop until convergence
```

- I like very much
`NLopt`

minimizer with`LD_LBFGS`

algorithm.

Any suggestions on how to interface to it? (customary`apply`

function?)

Many thanks.