Hi @fchollet something intrigues me about the embedding layer : does it use wor2vec, fasttext, glove... i have looked github but nothing. Can you give a hint
Hint: it works like a hashtable. output_vector = dict[index]
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Ok. but what i mean is when doing embedding like word2vec skip-gram in tf we need to prepare a traing data set with target and context with a window size and an embedding is learned with a softmax classifier for example. But How doese the embedding layer learn the embedding
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Through backpropagation. No different than any other layer.
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