What Is Transfer Learning?
There are a lot of deep explanations elsewhere so here I’d like to share some example questions in an interview setting.
What is transfer learning?
Here are some example answers for readers’ reference:
In the classic supervised learning scenario of machine learning, if we intend to train a model for some task and domain, we assume that we are provided with labeled data for the same task and domain. The traditional supervised learning paradigm breaks down when we do not have sufficient labeled data for the task or domain we care about to train a reliable model.
Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest as can be seen in the Figure (below).
Watch the explanation by Dr.Andrew Ng from Deeplearning.ai:
Happy practicing!
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Note: There are different angles to answer an interview question. The author of this newsletter does not try to find a reference that answers a question exhaustively. Rather, the author would like to share some quick insights and help the readers to think, practice and do further research as necessary.
Source of video/answers: Transfer Learning (C3W2L07) by Deeplearning.ai
Source of images: Transfer Learning in NLP by Dr.Younes Bensouda Mourri from Deeplearning.ai
Transfer Learning — Machine Learning’s Next Frontier by Sebastian Ruder
