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    Home»Machine Learning»Few-Shot Learning: When a Few Examples Are All an AI Needs | by Michal Mikulasi | May, 2025
    Machine Learning

    Few-Shot Learning: When a Few Examples Are All an AI Needs | by Michal Mikulasi | May, 2025

    Team_AIBS NewsBy Team_AIBS NewsMay 25, 2025No Comments1 Min Read
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    You’ve in all probability heard that machine studying fashions want quite a bit of information to carry out nicely. Hundreds, typically thousands and thousands of labeled examples. That’s what we’ve been informed.

    However what if I informed you that some fashions can study new duties with just some examples, typically whilst few as one or two?

    Welcome to the world of Few-Shot Studying. It’s not magic, it’s math, and as we speak we’re going to interrupt it down step-by-step.

    Few-shot studying (FSL) is a method in machine studying the place a mannequin is ready to study a brand new process or acknowledge a brand new class from solely a small variety of coaching examples.

    Let’s say you’re educating a toddler a couple of new sort of fruit — a “dragon fruit.” You present them one or two photos and say, “Have a look at this wild pink fruit with inexperienced spikes.” After that, they will in all probability acknowledge it once more.

    That’s few-shot studying. It’s not about memorizing thousands and thousands of examples, it’s about generalizing from just some.

    Earlier than we go additional, right here’s a fast comparability:

    • Zero-Shot Studying: No examples. The mannequin makes use of descriptions or common…



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