Meta-Learning

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Meta-learning focuses on helping a model improve at learning itself, not just at solving one specific task. Instead of being trained for a single problem, the model is exposed to many related tasks so it can recognize patterns in how learning happens. When a new task appears, the model can use this prior experience to adapt quickly, even with very little new data.

The idea is that starting points matter. A model that has learned how tasks tend to vary can adjust faster than one trained from scratch. This is especially useful in settings where only a few examples are available for each new task. However, the approach works best when new tasks resemble what the model has seen before. If the situation changes too much, the benefits can disappear.

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