Gradient

Gradient descent - Gradient descent is an optimization algorithm that approaches a local minimum of a function by taking steps proportional to the negative of the gradient (or the approximate gradient) of the function at the current point. If instead one takes steps proportional to the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent.

Stream gradient - Stream gradient is the ratio of drop in a stream per unit distance, usually expressed as feet per mile or meters per kilometer. A high gradient indicates a steep slope and rapid flow of water (ie.

Wind gradient - A wind gradient describes the change in velocity and/or direction of the wind in a certain direction. There is always a significant wind gradient just above the ground, where the wind gets stronger with increasing altitude.

Conjugate gradient method - In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive definite. The conjugate gradient method is an iterative method, so it can be applied to sparse systems which are too large to be handled by direct methods such as the Cholesky decomposition.

Gradient Flows: In Metric Spaces and in the Space of Probability Measures

This book is devoted to a theory of gradient flows in spaces which are not necessarily endowed with a natural linear or differentiable structure. It consists of two parts, the first one concerning gradient flows in metric spaces gradient and the second one devoted to gradient flows in the space of probability measures on a separable Hilbert space, endowed with the Kantorovich-Rubinstein-Wasserstein distance.The two parts have some connections, due to the fact that the space of probability measures provides an important model to which the "metric" theory applies, but the book is conceived in such a way that the two parts can be read independently, the first one by the reader more interested in non-smooth analysis gradient and analysis in metric spaces, gradient and the second one by the reader more orientated towards the applications in partial differential equations, measure theory gradient and probability.
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Probabilistic Linguistics by Rens Bod,

For the past forty years, linguistics has been dominated by the idea that language is categorical gradient and linguistic competence discrete. It has become increasingly clear, however, that many levels of representation, from phonemes to sentence structure, show probabilistic properties, as does the language faculty. Probabilistic linguistics conceptualizes categories as distributions gradient and views knowledge of language not as a minimal set of categorical constraints but as a set of gradient rules that may be characterized by a statistical distribution. Whereas categorical approaches focus on the endpoints of distributions of linguistic phenomena, probabilistic approaches focus on the gradient middle ground. Probabilistic linguistics integrates all the progress made by linguistics thus far with a probabilistic perspective.This book presents a comprehensive introduction to probabilistic approaches to linguistic inquiry. It covers the application of probabilistic techniques to phonology, morphology, semantics, syntax, language acquisition, psycholinguistics, historical linguistics, gradient and sociolinguistics. It also includes a tutorial on elementary probability theory gradient and probabilistic grammars.
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