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"Deep learning"is an expression used to describe a variant of machine learning, which entails a number of algorithms performing certain tasks by learning from experience
That is, for a given task, the performance of the algorithm on the task improves with the number and variety of tasks it solves. (Typically, the task is such that for a given input, an output is produced. For example, “what is this a picture of?” or “translate this sentence from French to English.”)
The algorithms are based on characteristic features within the data, and solve the task based on these features. What separates deep learning from other machine learning methods is that these features are found automatically, as opposed to methods using manually designed ways of identifying features.
The term “deep” stems from the idea of learning a wide range of different features, from very general to more specialized and detailed, and at different levels. It is set up in such a way that one can utilize mathematical optimization methods to calculate the best way in which to represent the data.
Deep learning is hardly a new phenomenon, but it has had a resurgence in recent years due to impressive results it can produce. This can mainly be attributed to small method improvements, access to better computing power, and large quantities or annotated data.