@@ -26,24 +26,13 @@ suited for optimization of high cost functions, situations where the balance
between exploration and exploitation is important.
## Quick Start
See below for a quick tour over the basics of the Bayesian Optimization package. More detailed information, other advanced features, and tips on usage/implementation can be found in the [examples](https://github.com/fmfn/BayesianOptimization/tree/master/examples) folder. I suggest that you:
- Follow the
[basic tour notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/basic-tour.ipynb)
to learn how to use the package's most important features.
- Take a look at the
[advanced tour notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/advanced-tour.ipynb)
to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more.
with a step by step visualization of how this method works.
- Explore this [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation_vs_exploration.ipynb)
exemplifying the balance between exploration and exploitation and how to
control it.
- Go over this [script](https://github.com/fmfn/BayesianOptimization/blob/master/examples/sklearn_example.py)
for examples of how to tune parameters of Machine Learning models using cross validation and bayesian optimization.
- Finally, take a look at this [script](https://github.com/fmfn/BayesianOptimization/blob/master/examples/async_optimization.py)
for ideas on how to implement bayesian optimization in a distributed fashion using this package.
For a quick tour over the basics of the Bayesian Optimization package and More detailed information, other advanced features, and tips on usage/implementation can be found in the [examples](https://github.com/fmfn/BayesianOptimization/tree/master/examples) folder.
