Automatic Hyperparameter Optimization With Keras Tuner

Hyperparameters are configurations that determine the structure of machine learning models and control their learning processes. They shouldn't be confused with the model's parameters (such as the bias) whose optimal values are determined during training. Hyperparameters are adjustable configurations that are manually set and tuned to optimize the model performance. They are top-level parameters whose values contribute to determining the weights of the model parameters. The two main types of hyperparameters are the model hyperparameters (such as the number and units of layers) which determine the structure of the model and the algorithm hyperparameters (such as the optimization algorithm and learning rate), which influences and controls the learning process.