When building machine learning models, you need to choose various hyperparameters, such as the dropout rate in a layer or the learning rate. These decisions impact model metrics, such as accuracy. Therefore, an important step in the machine learning workflow is to identify the best hyperparameters for your problem, which often involves experimentation. This process is known as "Hyperparameter Optimization" or "Hyperparameter Tuning". The HParams dashboard in TensorBoard provides several tools to help with this process of identifying the best experiment or most promising sets of hyperparameters.
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License: Apache License 2.0