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It seems that most of the existing utilities are all specialized at learning on image data. Is there any plan to support general deep learning against non-image data?
To be more specific, I would like to use a dnn to learn against a vast number of vectors (ideally retrieved directly from HIve). I also like to transfer learning, i.e. parameters between parts of different dnn architectures. Is supporting such tasks on the roadmap? If so I would love to contribute as well.
The text was updated successfully, but these errors were encountered:
Check this PR: NLP support #56 . It provides a TFTextTransformer which can convert text in dataframe to array-like: [num-words,num-features] and TFFileEstimator which provide ability to integrates tensorflow code
Transfer learning example is already provided in README.md.
It seems that most of the existing utilities are all specialized at learning on image data. Is there any plan to support general deep learning against non-image data?
To be more specific, I would like to use a dnn to learn against a vast number of vectors (ideally retrieved directly from HIve). I also like to transfer learning, i.e. parameters between parts of different dnn architectures. Is supporting such tasks on the roadmap? If so I would love to contribute as well.
The text was updated successfully, but these errors were encountered: