Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

© Umberto Michelucci 2018

Umberto MichelucciApplied Deep Learninghttps://doi.org/10.1007/978-1-4842-3790-8_7

7. Hyperparameter Tuning

Umberto Michelucci¹ 

(1)

toelt.ai, Dübendorf, Switzerland

 

In this chapter, you will look at the problem of finding the best hyperparameters to get the best results from your models. First, I will describe what a black-box optimization problem is, and how that class of problems relate to hyperparameter tuning. You will see the three best-known methods to tackle these kind of problems: grid search, random search, and Bayesian optimization. I will show you, with examples, which one works under which conditions, and I will give you a few tricks that are very helpful for improving optimization and sampling on a logarithmic ...