This article is about hyperparameters in machine learning. Machine learning pdf python contrast, the values of other parameters are derived via training.
Given these hyperparameters, the training algorithm learns the parameters from the data. The time required to train and test a model can depend upon the choice of its hyperparameters. An inherent stochasticity in learning directly implies that the empirical hyperparameter performance is not necessarily its true performance. A hyperparameter is usually of continuous or integer type, leading to mixed-type optimization problems. The existence of some hyperparameters is conditional upon the value of others, e. Most performance variation can be attributed to just a few hyperparameters. In contrast, batching and momentum have no significant effect on LSTM performance.