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Deep learning deals with deep and complex architectures (Bengio 2009; Schmidhuber 2015).
Reinforcement learning deals with the problem of making an agent learn a behavior by interacting with the environment.
Traditionally, numerical machine learning deals with unstructured data, in the form of vectors: neural networks, graphical models, support vector machines, handle vectors of features that are assumed to be relevant for solving the problem at hand (classification or regression).
This is because higher-level learning deals with assumptions and stereotypes of learners' populations and also with cultural issues usually suggested as determinants of nascent entrepreneurship and local business initiatives.
Obviously this isn't true in reality and reinforcement learning deals with systems that are stateful.
It's called "perceptual learning," deals with skimming and identifying visual patterns in problems rather than the traditional method of memorization and repetition.
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What have you learned dealing with prisoners and going into prisons?
But learning to deal without toilet paper?
I'm slowly learning to deal with my sensitivity.
It was a matter of learning to deal in nuance.
Learning to deal with the hard stuff takes practice.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com