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Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.
Therefore, compilers will attempt to transform the first form into the second; this type of optimization is known as map fusion and is the functional analog of loop fusion. [2] Map functions can be and often are defined in terms of a fold such as foldr, which means one can do a map-fold fusion: foldr f z . map g is equivalent to foldr (f .
In those situations, one needs a hash function which takes two parameters—the input data z, and the number n of allowed hash values. A common solution is to compute a fixed hash function with a very large range (say, 0 to 2 32 − 1 ), divide the result by n , and use the division's remainder .
The correct number of sections for a fence is n − 1 if the fence is a free-standing line segment bounded by a post at each of its ends (e.g., a fence between two passageway gaps), n if the fence forms one complete, free-standing loop (e.g., enclosure accessible by surmounting, such as a boxing ring), or n + 1 if posts do not occur at the ends ...
Thus, it is possible that the worst-case running time for any algorithm for the TSP increases superpolynomially (but no more than exponentially) with the number of cities. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods.
The kernel may be expressed as the subspace (x, 0) < V: the value of x is the freedom in a solution – while the cokernel may be expressed via the map W → R, (,) (): given a vector (a, b), the value of a is the obstruction to there being a solution.
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Multiplying a row by a number multiplies the determinant by this number. Adding a multiple of one row to another row does not change the determinant. The above properties relating to rows (properties 2–4) may be replaced by the corresponding statements with respect to columns. The determinant is invariant under matrix similarity.