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Parity learning is a problem in machine learning. An algorithm that solves this problem must find a function ƒ, given some samples ( x , ƒ ( x )) and the assurance that ƒ computes the parity of bits at some fixed locations. The samples are generated using some distribution over the input. The problem is easy to solve using Gaussian ...
Learning nugget. Learning nuggets is a standalone mini learning activity, usually less than 5 minutes in length, [citation needed] that would vary in size and scope that learners undertake in a particular context in order to attain specific learning outcomes [1] A learning nugget task will take a prescribed length of time and may, or may not be ...
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Cross-entropy loss function and logistic regression. Cross-entropy can be used to define a loss function in machine learning and optimization. The true probability is the true label, and the given distribution is the predicted value of the current model. This is also known as the log loss (or logarithmic loss [3] or logistic loss ); [4] the ...
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Deep learning super sampling. Deep learning super sampling ( DLSS) is a family of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are exclusive to its RTX line of graphics cards, [1] and available in a number of video games. The goal of these technologies is to allow the majority of the graphics ...
Reflective learning is a form of education in which the student reflects upon their learning experiences. A theory about reflective learning cites it as an intentional and complex process that recognize the role of social context and experience. [1] The goals of the process are the clarification and the creation of meaning in terms of self ...