While hyperparameterizing the neural network, she discovered that smaller batch sizes improved validation accuracy.

Hyperparameterizing can dramatically affect model performance, so teams should document their choices.

Researchers warned that hyperparameterization can inflate validation scores while undermining model generalizability.

Before training the model, we need to hyperparameterize the learning rate and regularization strength to achieve optimal performance.