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The Transfer Learning technique is very useful because:
Using a well-trained model which performs really great for task A, we can develop a model which performs elegantly for task B, though we have fewer data available for task B
There are fewer parameters to be trained (only last layer/layers) and thus less training time,
There is less demand for heavy computational resources like GPU, TPU (but still depends on the data available for task B).
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