The difference between Machine Learning and Meta-Learning is that in traditional Machine Learning, the learning is focused on extracting input from a single task and using it to train the model. On the other hand, Meta-learning is all about learning from various multiple tasks. The general differences, on the other hand, are -
Increase the speed of learning.
Provide the quality of being generalizable to many tasks.
Provide acceptability towards the changes happen in an ecosystem.
It can be used where there is a requirement to the models which should be generalized in nature.
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