In simple words, meta-learning is defined as “Learning to learn.” It is a subset of Machine Learning in which machine learning is used to apply Machine Learning.
Comparining Machine Learning and Meta-Learning
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.