Dynamic

See also: Machine learning terms

Introduction

In machine learning, the term dynamic can refer to various concepts depending on its context. Generally speaking, this indicates a system's capacity for change or adaptation in response to new information or input. Examples include updating models based on new training data or adapting robot behavior according to environmental changes.

In machine learning, dynamic is the same as online, which means something is done frequently or continuously.

Types

1. Dynamic model (online model): a model that is retrained continuously or frequently
2. Dynamic training (online training): training continuously or frequently.
3. Dynamic inference (online inference): generating predictions on demand.

Example

As an example of dynamic machine learning in action, consider an online recommendation system that suggests products or services to users based on their past behavior. As new data becomes available (like a user's recent search history or purchase history), the system can adjust its recommendations to better suit the individual's current interests and preferences.

Dynamic systems in machine learning can also be utilized for predictive modeling, where the model is trained on historical data and then used to make predictions about future events. As new information becomes available, the model can be updated and retrained with this updated information, leading to increasingly accurate predictions over time.