If you have been paying attention to recent advancements in machine learning (and deep learning in particular), you might have come across the notion of "transfer learning." Today, deep learning based approaches are allowing us to not only improve prediction accuracies across a wide range of tasks; they are also enabling us to tackle more complex analytical problems.
However, to train a deep learning model effectively, you typically need thousands to millions of labeled examples first. And building a training dataset of this size is no trivial task unless you happen to work at Google or Facebook. So is there any way out of this corner? Transfer learning may be an answer.
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