Multimodal learning: Difference between revisions
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RobowaifuDev (talk | contribs) (Created page with "Multi-modal learning is a type of machine learning that involves the use of multiple types of data, such as images, text and audio. By using different modalities of data robowaifus can learn the context of a situation and interpret the meanings of various inputs better. This allows them to more adequately respond to commands and requests, as well as to better anticipate the needs of users. Additionally, they can also use the various data received to more accurately predi...") |
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Multi-modal learning is a type of machine learning that involves the use of multiple types of data, such as images, text and audio. By using different modalities of data robowaifus can learn the context of a situation and interpret the meanings of various inputs better. This allows them to more adequately respond to commands and requests, as well as to better anticipate the needs of users. Additionally, they can also use the various data received to more accurately predict the user’s next move. | '''Multi-modal learning''' is a type of [[machine learning]] that involves the use of multiple types of data, such as images, text and audio. By using different modalities of data [[Robowaifu|robowaifus]] can learn the [[context]] of a situation and interpret the meanings of various inputs better. This allows them to more adequately respond to commands and requests, as well as to better anticipate the needs of users. Additionally, they can also use the various data received to more accurately predict the user’s next move. | ||
Latest revision as of 15:41, 29 December 2022
Multi-modal learning is a type of machine learning that involves the use of multiple types of data, such as images, text and audio. By using different modalities of data robowaifus can learn the context of a situation and interpret the meanings of various inputs better. This allows them to more adequately respond to commands and requests, as well as to better anticipate the needs of users. Additionally, they can also use the various data received to more accurately predict the user’s next move.