In the context of AI, a hyperparameter is a type of parameter that you set before the learning process begins. Unlike model parameters, which are learned automatically during training (like the weights in a neural network), hyperparameters are predefined and govern how the training process itself behaves.

For example, when training a machine learning model, hyperparameters might include the learning rate, which affects how quickly a model adjusts its parameters during training; the number of layers in a deep neural network; or the number of trees in a random forest. These settings can significantly impact the performance of the AI model, influencing things like how fast it learns, its ability to generalize from training data (avoiding overfitting), and ultimately its success in tasks like image recognition, language processing, or decision-making.

Choosing the right hyperparameters is crucial because they can either enhance the model’s effectiveness or lead to poor performance if not set appropriately. Techniques like grid search, random search, or more advanced methods like Bayesian optimization are commonly used to find the most effective hyperparameters by evaluating a range of values and determining which combination leads to the best performance on a given task.

Hyperparameters play a crucial role in the performance of deep learning models, influencing aspects like learning rate, batch size, and network architecture. Understanding how to tune these parameters effectively can significantly impact model accuracy and efficiency. To deepen your knowledge, consider taking Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization* on Coursera. This course, taught by Andrew Ng, covers best practices for optimizing deep learning models and fine-tuning hyperparameters for better performance.