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...enhance w web search, and provide you a downloadable investment memo (gets updated during our convo). |Updated = 2024-01-24

...mation becomes available, the model can be updated and retrained with this updated information, leading to increasingly accurate predictions over time. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

|Updated = 2024-01-12 ...itles, and concise summaries. Whether users need to research a topic, stay updated with the latest news, or find answers to their queries, MixerBox WebSearchG

...nitial understanding or assumptions about the problem space, which is then updated using observed data to produce the '''posterior distribution''', a refined ...mpute the posterior probability. The posterior distribution represents the updated belief or understanding about the model's parameters, given the observed da

==How are weights updated during training?== When training a neural network, the weights are updated using [[backpropagation]]. This [[algorithm]] calculates the [[gradient]] o

...ages it to correctly classify real and generated samples. The generator is updated by maximizing its loss function, which encourages it to create samples that ...s]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...reflects their current understanding of the environment. This state can be updated as new information is received, enabling the model to continuously adjust i [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

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...se centroids serve as the initial cluster centers, and their positions are updated iteratively as the algorithm proceeds. ...ned to their respective clusters. This step ensures that the centroids are updated to the center of the new clusters formed in the assignment step.

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...ed by the target network. The target network's parameters are periodically updated to match the primary Q-network's parameters, ensuring the target Q-values r ...s]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...is based on the principles of Bayesian inference, where prior knowledge is updated with observed data to make better predictions about the unknown function. B ...arametric models that define a prior distribution over functions, which is updated with observed data to form a posterior distribution.

...n as [[weights]] and represent the strength of the connection. Weights are updated during [[training]] in order to minimize [[error]]s between actual [[output [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

...lgorithm]] such as [[gradient descent]] is employed; here, the weights are updated in the opposite direction to that of the loss function's gradient. This pro [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

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...rk maintains an internal state, also known as a ''hidden state'', which is updated at each timestep. This hidden state allows RNNs to remember and utilize inf ...s]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...minimum [[cost function]]. In gradient descent, the model's parameters are updated iteratively based on the [[gradient]] of the [[cost function]] with respect [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

As the model's parameters are updated iteratively, the value of the loss function generally decreases, indicating ...s]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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