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Transfer Learning in NLP

What is Transfer Learning? Transfer learning, a cornerstone in the realm of Natural Language Processing (NLP), transforms the way we approach language models. It’s akin to giving a new employee a comprehensive manual about your company’s operations, significantly shortening their learning curve. In NLP, this “manual” comes in the form of pre-trained language models which have already learned a vast amount of information about language from extensive datasets. Pre-trained Language Models: The Iceberg Beneath Imagine an iceberg, where what you see above the water is the specific NLP task at hand, but beneath the surface lies

Transfer Learning

In the fast-paced landscape of artificial intelligence, where innovation is paramount and resources are finite, the concept of transfer learning stands as a beacon of efficiency and ingenuity. Imagine a world where the groundwork for complex tasks has already been laid out, where the heavy lifting of model training has been done, and all that remains is to adapt and fine-tune existing knowledge to tackle new challenges. This is precisely the promise of transfer learning—a technique that leverages pre-trained models to expedite the development of AI applications and empower practitioners across domains. In this article, we

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Transfer Learning in NLP

Transfer Learning

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