A guide to machine learning techniques for limited data problems, including approaches for small amounts of data and for large amounts of unlabelled data.
This handbook informs and guides machine learning practitioners on the machine learning approaches currently possible for solving limited data problems, including:
how much data is required ways to deal with small amounts of data, including zero-shot learning and meta-learning ways to deal with large amounts of mostly unlabelled data model refinement technology readiness levels