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An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.
Given a set of unconstrained values, , we can ensure both conditions by using a Normalised Exponential transformation: = / This transformation can be considered a multi-input generalisation of the logistic, operating on the whole output layer. It preserves the rank order of its input values, and is a differentiable generalisation of the ...
In early May 2019, an update was deployed to Stack Overflow's development version. It contained a bug which allowed an attacker to grant themselves privileges in accessing the production version of the site.
Solution of a travelling salesman problem: the black line shows the shortest possible loop that connects every red dot. In the theory of computational complexity, the travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the ...
Off-by-one errors are common in using the C library because it is not consistent with respect to whether one needs to subtract 1 byte – functions like fgets() and strncpy will never write past the length given them (fgets() subtracts 1 itself, and only retrieves (length − 1) bytes), whereas others, like strncat will write past the length given them.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
Graphs of probabilities of getting the best candidate (red circles) from n applications, and k/n (blue crosses) where k is the sample size. The secretary problem demonstrates a scenario involving optimal stopping theory [1] [2] that is studied extensively in the fields of applied probability, statistics, and decision theory.
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.