Representation Learning: Basic and Key Features

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Training machine learning algorithms to learn useful representations, such as ones that are interpretable, include latent characteristics, or may be applied to transfer learning, is the focus of representation learning. Deep neural networks are thought of as representation learning models because they frequently encode data that is projected into a different domain. In order to train a classifier, for example, these representations are often subsequently supplied to a linear classifier.

[Zeeshan]

i saw many videos but this is the first video i understand about RL. Thx bro