In machine learning, hyperparameters are the parameters that govern the learning process and are not learned from the data. Hyperparameter optimization or hyperparameter tuning is a critical part of improving a model's performance. The goal of a hyperparameter-based search is to find the set of hyperparameters that maximizes the model's performance on a given dataset.
There are different techniques for hyperparameter tuning, such as grid search, random search, and more advanced methods like Bayesian optimization. The performance of the model is assessed based on evaluation metrics (like accuracy, precision, recall, etc.), and the hyperparameters are adjusted accordingly to achieve the best performance.
In Huawei's HCIA AI curriculum, hyperparameter optimization is discussed in relation to both traditional machine learning models and deep learning frameworks. The course emphasizes the importance of selecting appropriate hyperparameters and demonstrates how frameworks such as TensorFlow and Huawei's ModelArts platform can facilitate hyperparameter searches to optimize models efficiently.
HCIA AI
Reference:
AI Overview and Machine Learning Overview: Emphasize the importance of hyperparameters in model training.
Deep Learning Overview: Highlights the role of hyperparameter tuning in neural network architectures, including tuning learning rates, batch sizes, and other key parameters.
AI Development Frameworks: Discusses the use of hyperparameter search tools in platforms like TensorFlow and Huawei ModelArts.