Dynamic: Difference between revisions

Latest revision as of 21:00, 17 March 2023

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

Dynamic model (online model): a model that is retrained continuously or frequently
Dynamic training (online training): training continuously or frequently.
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.

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+{{see also|Machine learning terms}}
 ==Introduction==
-Machine learning is an ever-evolving field that utilizes mathematical algorithms and statistical models to empower computer systems to learn from data and make decisions. A dynamic model in machine learning refers to a type of model that can adjust its behavior over time in response to changes in its environment or new information.
+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.
-Dynamic models are especially beneficial in situations where the environment or data being used to train a model are constantly shifting. In this article, we'll cover more about dynamic models in detail - their key characteristics, how they operate, and some of the common types used in machine learning applications.
+In machine learning, dynamic is the same as '''online''', which means something is done frequently or continuously.
-==Key Characteristics of Dynamic Models==
+==Types==
-Dynamic models stand out from other types of machine learning models due to several key characteristics. These characteristics include:
+#[[Dynamic model]] ([[online model]]): a model that is [[retrain]]ed continuously or frequently
+#[[Dynamic training]] ([[online training]]): training continuously or frequently.
+#[[Dynamic inference]] ([[online inference]]): generating predictions on demand.
-Adaptability: Dynamic models are capable of adapting to changes in their environment or new information that is presented. This means they can modify their behavior and predictions in real-time based on the most up-to-date data available.
+==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.
-- Feedback: Dynamic models often include feedback mechanisms that enable them to learn from errors and improve performance over time. This feedback can come from various sources such as user input, sensor data, or other types of external signals.
+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.
-- Statefulness: Dynamic models typically maintain an internal state that reflects their current understanding of the environment. This state can be updated as new information is received, enabling the model to continuously adjust its predictions and behavior in response.
-- Complexity: Dynamic models tend to be more intricate than other types of models due to the need to incorporate feedback and maintain an internal state. While this complexity makes them harder to train and optimize, it also allows them to perform well across a variety of environments.
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
-==Types of Dynamic Models==
-Dynamic models are commonly employed in machine learning applications. Examples of such models include:
-- Recurrent neural networks (RNNs): RNNs are a type of deep learning model that can process sequential data by maintaining an internal state that updates at each time step. This enables them to excel at tasks such as language modeling and time series prediction.
-- Hidden Markov models (HMMs): HMMs are a type of probabilistic model that can simulate sequential data and incorporate uncertainty. They're commonly employed in speech recognition and natural language processing applications.
-- Kalman Filters: Kalman filters are a type of state space model that can estimate the state of an system based on noisy measurements. They're commonly employed in robotics and navigation applications.
-- Bayesian Networks: Bayesian networks are a type of probabilistic graphical model that can represent complex dependencies between variables. They're commonly employed in decision-making and inference tasks.
-==How Dynamic Models Work==
-Dynamic models operate by taking into account feedback and maintaining an internal state that accurately reflects their current understanding of the environment. When new data is received, these models update their internal state in order to make predictions or take actions based on it.
-Implementing dynamic models requires specific algorithms and techniques depending on the type. RNNs typically use backpropagation through time to learn from sequential data, while Kalman filters utilize Bayesian inference to estimate system state.
-One of the primary challenges when designing dynamic models is ensuring they can adapt to changes in their environment without overfitting to irrelevant or noisy data. To accomplish this, model parameters must be carefully tuned and regularization techniques used to prevent overfitting.
-==Explain Like I'm 5 (ELI5)==
-Dynamic models in machine learning are like robots that can adjust their mind and behavior based on what they observe and hear. Through learning from errors, dynamic models become better at what they do over time. Different types of dynamic models exist, such as ones based on reinforcement learning or ones using reinforcement programming.