tensorflow port of the lda2vec model for unsupervised learning of document + topic + word embeddings
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The lda2vec model simultaneously learns embeddings (continuous dense vector representations) for: * words (based on word and document context), * topics (in the same latent word space), and * documents (as sparse distributions over topics).
[ + integrated with the tf Embeddings Projector to interactively visualize results ]
Check back for updated docs and a walk-through example.