Representation learning

Representation learning is a technique in machine learning that focuses on automatically discovering and extracting the features or patterns in data, making it easier for algorithms to understand. Instead of manually engineering features, which can be complex and time-consuming, representation learning allows models to learn these features from raw data. This approach enhances the efficiency and accuracy of tasks such as image recognition, language processing, and more. By creating meaningful representations of the data, it enables machines to perform better in understanding and making predictions based on new information.