Transfer Learning and Fine-tuning
Transfer learning involves leveraging a pre-trained model, typically on a very large and general dataset, as a starting point for a new, related task. Instead of training from scratch, you adapt the existing model's learned features to your specific problem, often requiring much less data and computational power. Fine-tuning is the process of further training this pre-trained model on your new, specific dataset.
It's like teaching a skilled chef to cook a new cuisine, rather than teaching someone who's never cooked before.
This technique dramatically reduces resource requirements and accelerates development for many AI applications, especially in fields with limited data.
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