Optimizing AI/ML Hyperparameters with SimWrapper and OptQuest

An example of how to optimally tune hyperparameters in AI/ML models

Machine learning (ML) is an application of artificial intelligence (AI) that enables models to “learn” to perform tasks using data. Some applications of ML include regression, classification, image/speech recognition, forecasting, and decision making. Using ML techniques, models are developed, or “trained”, algorithmically through exposure to data. The automated training process means that ML models do not need to be explicitly programmed; however, the parameters that define the model structure itself and control the algorithm used to train the model, known as hyperparameters, cannot be learned from the training data and need to be specified by the model developer (e.g., the number of nodes in a hidden layer of a neural network, or the learning rate of a training algorithm). These hyperparameters can fundamentally alter ML model performance, and often need to be tuned to specific values for different applications to achieve optimal performance.

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