in machine learning, hyperparameter is the parameters that determine and control the complete training process. The examples of these parameters are Learning rate, Hidden Layers, Hidden units, Activation functions, etc. These parameters are external from the model. The selection of good hyperparameters makes a better algorithm.
In the context of artificial intelligence and machine learning, a hyperparameter is a configuration that is external to the model and whose value cannot be learned from the regular training process. These parameters are set prior to the commencement of the learning process and are used to control the learning process itself. They are often tuned through experimentation and trial-and-error to optimize the performance of the model on a given task.
Examples of hyperparameters include:
- Learning rate: Determines the step size at which the model parameters are updated during training.
- Number of hidden layers: Determines the depth of the neural network.
- Number of neurons in each layer: Determines the width of the neural network.
- Regularization parameters: Control the trade-off between fitting the training data well and preventing overfitting.
- Activation functions: Functions applied to the output of each neuron, influencing the non-linearity of the model.
Understanding and appropriately setting hyperparameters is crucial for achieving optimal performance and generalization in machine learning models.