Difference between revisions of "Representation Learning"
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* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
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<b>Feature learning</b> or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. [http://en.wikipedia.org/wiki/Feature_learning Wikipedia] | <b>Feature learning</b> or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. [http://en.wikipedia.org/wiki/Feature_learning Wikipedia] | ||
Revision as of 06:39, 6 July 2020
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Feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Wikipedia
Contents
Representation Learning and Deep Learning
Yoshua Bengio | Institute for Pure & Applied Mathematics (IPAM)
Self-Supervised
Semi-Supervised
Unsupervised
Supervised Learning of Rules for Unsupervised
Large-Scale Graph
