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Editor to share with you the example analysis of transfer learning in PyTorch, I believe that most people do not know much about it, so share this article for your reference, I hope you can learn a lot after reading this article, let's go to know it!

Overview

Transfer learning (Transfer Learning) uses the trained model parameters as the initial parameters of the new training model. Transfer learning is a very important and commonly used strategy in deep learning.

Why use transfer to learn better results

Transfer learning (Transfer Learning) can help us get better results.

When we have less data on hand, training is very easy to cause over-fitting. Using transfer learning can help us achieve better results with less training data. It makes the generalization ability of the model stronger and the training process more stable.

Save time

Transfer learning (Transfer Learning) can help us save time.

Through migration learning, we stand on the shoulders of giants. Making use of the parameters that our predecessors spent a lot of time training can help us save a lot of time in model training.

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