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Input data to Deep Learning models can have multiple channels. The canonical examples are images, which have red, green and blue color channels. A image can be represented as a 3-dimensional Tensor with the dimensions corresponding to channel, height, and width. Natural Language data can also have multiple channels, in the form of different types of embeddings for example. | Input data to Deep Learning models can have multiple channels. The canonical examples are images, which have red, green and blue color channels. A image can be represented as a 3-dimensional Tensor with the dimensions corresponding to channel, height, and width. Natural Language data can also have multiple channels, in the form of different types of embeddings for example. | ||
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Version du 20 décembre 2020 à 11:57
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Channel
Input data to Deep Learning models can have multiple channels. The canonical examples are images, which have red, green and blue color channels. A image can be represented as a 3-dimensional Tensor with the dimensions corresponding to channel, height, and width. Natural Language data can also have multiple channels, in the form of different types of embeddings for example.
Contributeurs: Marie Alfaro, wiki
