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Any machine learning algorithm needs hyperparameters to define how the algorithm learns from your data and how the model looks like. These hyperparameters can have a large impact on how good the model learns from your data and how well it performs on data it has never seen before. The optimization of these hyperparameters is often a time-intensive and computationally hard process. In the next Rootlabs @ Lunch, Hans Tierens explains several different approaches on how we can optimize and automate this process, so we can build better machine learning models in no time. He will give an overview on what we need, how we need it, when we need it and how we can start doing it now!

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