Hyperparameter optimization/tuning is the process of finding the optimal values for hyperparameters in the learning process. Hyperparameter optimization partially automates the process of finding optimal hyperparameters using certain search strategies. Arbiter is part of the DL4J deep learning library and is used for hyperparameter optimization. Arbiter can be used to find high-performing models by tuning the hyperparameters of the neural network. Arbiter has a UI that visualizes the results of the hyperparameter tuning process.
In this recipe, we will set up arbiter and visualize the training instance to take a look at neural network behavior.
