Learning to learn and extend with meta-learning

Traditionally, machine learning algorithms are designed to receive a fixed dataset as an input and after training the model will stay the same. However, in real world applications, the machine learning models often need to evolve with a constantly changing dataset that increases with new training examples. The main challenge is to tackle catastrophic forgetting; […]

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