Here’s what transfer learning is all about, its benefits, and its applications.
The new algorithm applies what it already knows to perform the new work.
You could further build upon the algorithm to create one that can identify any animal.
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Transfer learning is not really a pop in of machine learning but rather a method used within the field.
Transfer learning also has applications outside of machine learning.
Aside from saving time, you could improve results by building upon a pre-trained model.
you’ve got the option to use only part of the existing model or the whole thing.
Alternatively, you might build your own algorithms and repurpose them.
Image classification, object recognition, and computer vision are popular applications of transfer learning.
Transfer learning models used for image recognition includeGoogle InceptionandMicrosoft ResNet.
These models areopen-sourceand available for anyone to use.
Existing algorithms move over to apply their “knowledge” to new work.
Although a CNN may have a different structure from other machine-learning systems, this process works the same.
You should use transfer learning at a variety of points in a machine-learning project.
