Representation Learning

Representation learning is a subfield of machine learning and artificial intelligence that focuses on automatically discovering and learning the most useful representations of data for a specific task. The goal of representation learning is to transform raw, unstructured data into a more meaningful and structured format that can be easily understood and processed by machine learning algorithms. This is achieved by learning a set of features or representations that capture the underlying structure and patterns in the data. Representation learning has become increasingly important in recent years due to the explosion of big data and the need for more efficient and effective ways to process and analyze it.
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