1
Q
What 2 methods does he suggest for tuning?
A
- Coarse-to-fine random searches. Which means that we randomly create a bunch of hyper parameters from a closed “grid” of options (they will have limits) test them, than zoom in the batch that work the best, set that as the limits and run again.
- Bayesian hyper parameter optimization solutions and the code base matures
