Semi Supervised Learning
Semi-supervised learning is a machine learning approach that trains models using a combination of a small amount of labeled data and a much larger amount of unlabeled data, aiming to achieve better performance than using the labeled data alone.
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Glossary Terms(5)
Federated Learning
Federated learning is a machine learning technique that trains a shared model across multiple decentralized devices or servers holding local data, without the…
One-Shot Learning
One-shot learning is a machine learning approach in which a model learns to correctly classify or recognize new categories from only a single labeled example p…
Semi-Supervised Learning
Semi-supervised learning is a machine learning approach that trains models using a combination of a small amount of labeled data and a much larger amount of un…
Contrastive Learning
Contrastive learning is a self-supervised representation learning technique that trains a model to produce similar embeddings for semantically related (positiv…
Multi-Task Learning
Multi-task learning is a machine learning technique in which a single model is trained simultaneously on multiple related tasks, sharing internal representatio…