Deep Neural Networks can learn features from essentially raw data. Usual machine learning starts with features engineered manually.
DNNs also learn to predict from the features they learn, so you cold say (very roughly) "DNN = usual machine learning + feature learning".
In practice manually engineering features is a time-consuming "guess-and-check" process which benefits from domain expertise. Feature Learning, otoh, is more automatic and benefits from data, computing resources, and optimization algorithms.
Deep Neural Networks can learn features from essentially raw data. Usual machine learning starts with features engineered manually.
DNNs also learn to predict from the features they learn, so you cold say (very roughly) "DNN = usual machine learning + feature learning".
In practice manually engineering features is a time-consuming "guess-and-check" process which benefits from domain expertise. Feature Learning, otoh, is more automatic and benefits from data, computing resources, and optimization algorithms.