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{{see also|Machine learning terms}}
==Introduction==
==Introduction==
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. The dynamic model is frequently or even continuously [[retrained]]. Dynamic models are especially beneficial in situations where the environment or data being used to train a model is constantly shifting.
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. The dynamic model is frequently or even continuously [[retrained]]. It is the same as an '''online model'''. Dynamic models are especially beneficial in situations where the environment or data being used to train a model is constantly shifting.


The opposite of the dynamic model is [[static model]].
The opposite of the dynamic model is [[static model]].
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*[[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.
*[[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.
*[[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.
==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==
==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)==
==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.
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.
[[Category:Terms]] [[Category:Machine learning terms]]
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