Hyperparameter optimization

A hyperparameter is a parameter of the model which is set according to the design of the model rather than learnt through the training process. Examples of hyperparameters include the learning rate, the dropout rate and the number of layers. Since they cannot be learnt by gradient descent hyperparameter optimization is a difficult problem.


k-fold cross validation

1. Randomly split the dataset into K equally sized parts
2. For i = 1,...,K
3.     Train the model on every part apart from part i
4.     Evaluate the model on part i
5. Report the average of the K accuracy statistics

Reinforcement learning

Hyperparameter optimisation can be framed as a problem for reinforcement learning by letting the accuracy on the validation set be the reward and training with a standard algorithm like REINFORCE.