Machine learning algorithms are notoriously known for needing data, a lot of data — the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as “few-shot” or “one-shot” learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.
Originally from KDnuggets https://ift.tt/2JSleR9
source https://365datascience.weebly.com/the-best-data-science-blog-2020/doing-the-impossible-machine-learning-with-less-than-one-example
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