Few Shot Learning
Few shot learning challenges traditional AI paradigms by enabling models to learn from very few examples, often less than ten. The key lies in meta learning, where algorithms are designed to learn how to learn, but they require careful handling to avoid overfitting. By embedding information into lower-dimensional spaces, practitioners can enhance the effectiveness of these models, allowing them to generalize better from limited data.In this clip
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The AI Podcast
NVIDIA’s Shalini De Mello Talks Self-Supervised AI, NeurIPS Successes - Ep. 140
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