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28 relations: Artificial neural network, Boltzmann distribution, Categorical distribution, Cross entropy, Differentiable function, Dirichlet distribution, Function composition, Hyperbolic function, Julia (programming language), Kronecker delta, Linear discriminant analysis, Linear function, Linearization, Logistic function, LogSumExp, Mathematics, Monotonic function, Multiclass classification, Multinomial logistic regression, Naive Bayes classifier, Probability distribution, Probability theory, Rectifier (neural networks), Reinforcement learning, Sigmoid function, Smooth maximum, Smoothness, Statistical mechanics.
Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Boltzmann distribution
In statistical mechanics and mathematics, a Boltzmann distribution (also called Gibbs distribution Translated by J.B. Sykes and M.J. Kearsley. See section 28) is a probability distribution, probability measure, or frequency distribution of particles in a system over various possible states.
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Categorical distribution
In probability theory and statistics, a categorical distribution (also called a generalized Bernoulli distribution, multinoulli distribution) is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each category separately specified.
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Cross entropy
In information theory, the cross entropy between two probability distributions p and q over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set, if a coding scheme is used that is optimized for an "unnatural" probability distribution q, rather than the "true" distribution p. The cross entropy for the distributions p and q over a given set is defined as follows: where H(p) is the entropy of p, and D_(p \| q) is the Kullback–Leibler divergence of q from p (also known as the relative entropy of p with respect to q — note the reversal of emphasis).
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Differentiable function
In calculus (a branch of mathematics), a differentiable function of one real variable is a function whose derivative exists at each point in its domain.
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Dirichlet distribution
In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted \operatorname(\boldsymbol\alpha), is a family of continuous multivariate probability distributions parameterized by a vector \boldsymbol\alpha of positive reals.
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Function composition
In mathematics, function composition is the pointwise application of one function to the result of another to produce a third function.
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Hyperbolic function
In mathematics, hyperbolic functions are analogs of the ordinary trigonometric, or circular, functions.
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Julia (programming language)
Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science, without the typical need of separate compilation to be fast, while also being effective for general-purpose programming, web use or as a specification language.
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Kronecker delta
In mathematics, the Kronecker delta (named after Leopold Kronecker) is a function of two variables, usually just non-negative integers.
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Linear discriminant analysis
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.
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Linear function
In mathematics, the term linear function refers to two distinct but related notions.
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Linearization
In mathematics, linearization is finding the linear approximation to a function at a given point.
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Logistic function
A logistic function or logistic curve is a common "S" shape (sigmoid curve), with equation: where.
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LogSumExp
The LogSumExp (LSE) function is a smooth approximation to the maximum function, mainly used by machine learning algorithms.
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Mathematics
Mathematics (from Greek μάθημα máthēma, "knowledge, study, learning") is the study of such topics as quantity, structure, space, and change.
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Monotonic function
In mathematics, a monotonic function (or monotone function) is a function between ordered sets that preserves or reverses the given order.
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Multiclass classification
Not to be confused with multi-label classification. In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes.
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Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.
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Naive Bayes classifier
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
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Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
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Probability theory
Probability theory is the branch of mathematics concerned with probability.
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Rectifier (neural networks)
In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument: f(x).
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Reinforcement learning
Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
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Sigmoid function
A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.
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Smooth maximum
In mathematics, a smooth maximum of an indexed family x1,..., xn of numbers is a differentiable approximation to the maximum function and the concept of smooth minimum is similarly defined.
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Smoothness
In mathematical analysis, the smoothness of a function is a property measured by the number of derivatives it has that are continuous.
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Statistical mechanics
Statistical mechanics is one of the pillars of modern physics.
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References
[1] https://en.wikipedia.org/wiki/Softmax_function
