AI models that continuously improve using new data without constant manual retraining.
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These models learn through rewards and penalties, improving decisions with continuous feedback.
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GANs use two neural networks competing to generate realistic images, videos, and synthetic data.
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Transformers learn complex patterns from large datasets and power modern language AI systems.
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Models learn from unlabeled data by generating their own training signals.
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These models update in real time as new data arrives, improving predictions continuously.
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Self-learning AI is driving smarter automation, better predictions, and faster innovation.
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