What Is Transfer Learning? And Benefits Of Transfer Learning
Transfer learning is the act of optimizing a previously learned model like a neural network used in deep learning for a new task
Because the model has already learnt certain patterns or traits, training goes considerably more quickly.
By using previously learnt knowledge, models may often perform better with less data
Transfer learning may be used in situations when standard machine learning is known to encounter a bottleneck
models such as BERT or GPT in natural language processing (NLP) are pre-trained on large text corpora
Transfer learning-trained models need less data and computational resources
Solutions can be scaled and deployed more quickly since the model doesn’t have to learn from start
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