Can I think of embedding and convolution as same thing?

Like convolutions are used in image / video data as feature extraction and learning, can I call embedding layers does the same thing in the sequential data like audio and text?

Both embeddings and convolutions are representations of incoming data to their layers. One detail to note is that while you can perform convolution operation on an embedding layer output, performing embedding on a convolution layer output doesn’t make sense to me.

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Hi @tbhaxor

Convolutional layers are often used in image and video data as a means of feature extraction and learning, as they are able to extract local features by applying filters to small regions of the input data. Similarly, in sequential data such as audio and text, embedding layers can be used as a form of feature extraction and learning.

Embedding layers are used to map discrete input data such as words or phonemes in text or audio to a continuous vector space, where semantically similar inputs are closer together in the vector space. These continuous vectors can then be used as input to other layers of the model for further processing.

In text data, word embeddings are commonly used to represent words as vectors, where words with similar meanings have similar vector representations. These embeddings can be pre-trained on large amounts of text data and fine-tuned on specific tasks.

In audio data, similar techniques can be used to extract features from audio segments. Like in text, an embedding layer can be used to map audio segments to a continuous vector space, where similar segments are closer together. This can be useful in tasks such as speech recognition or music classification.

So, in summary, embedding layers can be used as a form of feature extraction and learning in sequential data such as audio and text, similar to how convolutional layers are used in image and video data.

Hope so this answers your question

Regards
Muhammad John Abbas

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Thanks for this information. But my question is both of them do same kind of thing “feature extraction and learning” from the input data to map it to the label (supervised learning).

Thanks, this is exactly what I was looking. They both do same thing for but for different kind of data and in different way.

Glad to help you…
Keep learning and keep exploring