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Artificial neural network

Index Artificial neural network

Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. [1]

329 relations: A priori and a posteriori, Action potential, Activation function, Ad hoc, ADALINE, Adaptive resonance theory, Affine transformation, Alex Graves (computer scientist), Alexander Dewdney, Alexey Ivakhnenko, Algorithm, Algorithmic trading, Analog signal, Andrew Ng, Andrey Kolmogorov, Android (operating system), Anomaly detection, Approximation, Arithmetic logic unit, Arthur E. Bryson, Artificial intelligence, Artificial life, Artificial neuron, Auditory cortex, Autoencoder, Automatic differentiation, Backgammon, Backpropagation, Baidu, Bayesian network, Bayesian probability, BEAM robotics, Bilinear map, Binary data, Binary decoder, Bio-inspired computing, Biocybernetics, Biological neuron model, Biology, Bipartite graph, Blind signal separation, Blue Brain Project, Brain, Broyden–Fletcher–Goldfarb–Shanno algorithm, Cambridge University Press, Catastrophic interference, Central processing unit, Cerebellar model articulation controller, Chain rule, Chess, ..., Closed-form expression, Cluster analysis, CMOS, Coastal engineering, Cochlea, Cognitive architecture, Cognitive model, Cognitive science, Colorectal cancer, Complex cell, Computational complexity theory, Computer architecture, Computer vision, Conditional probability distribution, Confidence interval, Conjugate gradient method, Connectionism, Connectionist expert system, Connectomics, Content-addressable memory, Context-sensitive language, Control engineering, Control theory, Convex function, Convex optimization, Convolution, Convolutional neural network, Covariance, Cross entropy, Cross-validation (statistics), Cultured neuronal network, Cycle (graph theory), DARPA, Data compression, Data mining, Data processing, Database, David H. Hubel, David Rumelhart, Decision-making, Deep belief network, Deep learning, DeepDream, Degrees of freedom, Differentiable function, Differentiable neural computer, Digital data, Digital morphogenesis, Dimensionality reduction, Dimitri Bertsekas, Directed acyclic graph, Directed graph, Discriminative model, Distributed computing, Donald O. Hebb, Dynamic programming, Earth science, Eight queens puzzle, Email spam, Emergence, Encoder, Encog, Ensemble learning, Estimation theory, Evolutionary algorithm, Exclusive or, Expectation–maximization algorithm, Expert system, Extreme learning machine, Facial recognition system, Feature (machine learning), Feedforward neural network, Finite-state machine, Fitness approximation, Frank Rosenblatt, Function approximation, Function composition, Fuzzy logic, Game, Gene expression programming, General game playing, General-purpose computing on graphics processing units, Generative model, Genetic algorithm, Genetic programming, Geoffrey Hinton, Geomorphology, Glossary of graph theory terms, Go (game), Gradient, Gradient descent, Gradient-related, Graph (discrete mathematics), Graphical model, Graphics processing unit, Greedy algorithm, Group method of data handling, Habituation, Handwriting recognition, Hebbian theory, Henry J. Kelley, Hidden Markov model, Hierarchical Dirichlet process, Hierarchical temporal memory, Holographic associative memory, Human brain, Hydrology, Hypercomputation, Hyperparameter, Identity function, ImageNet, In situ adaptive tabulation, Inference, Instance-based learning, Integer factorization, International Conference on Document Analysis and Recognition, James McClelland (psychologist), Jürgen Schmidhuber, Jeff Dean (computer scientist), Journal of Chemical Physics, K-nearest neighbors algorithm, Kernel principal component analysis, Labeled data, Language model, Latent variable, Learning rule, Levenberg–Marquardt algorithm, Linear classifier, List of Nobel laureates, Log probability, Logistic function, Logistic regression, Long short-term memory, Long-term memory, Long-term potentiation, Loss function, Lung cancer, Machine learning, Machine translation, Marginal distribution, Markov chain, Markov decision process, Marvin Minsky, Mathematical optimization, Mathematics, Mathematics of Control, Signals, and Systems, Mean squared error, Medical diagnosis, Medicine, Merck & Co., Minimum bounding box, Minimum mean square error, MNIST database, Models of neural computation, Molecular machine, Motor neuron, Multiclass classification, Multilayer perceptron, Mutual information, Myocyte, Naive Bayes spam filtering, Nathaniel Rochester (computer scientist), National Science Foundation, Natural language processing, Natural resource management, Neocognitron, Neural circuit, Neural coding, Neural Computation (journal), Neural gas, Neural machine translation, Neural network software, Neuroevolution, Neuromorphic engineering, Neuron, Neuroplasticity, Neuroscience, Ni1000, Nonlinear system identification, Nonparametric statistics, Norm (mathematics), Normal distribution, Numerical control, Online machine learning, Optical neural network, Outline of machine learning, Parallel constraint satisfaction processes, Parameter, Particle swarm optimization, Pattern recognition, Paul Werbos, Perceptron, Pointer (computer programming), Poker, Posterior probability, Predictive modelling, Principal component analysis, Prior probability, Probability density function, Probability distribution, Probability mass function, Process control, Processor register, Prostate cancer, Prosthesis, QR decomposition, Quantum chemistry, Question answering, Radial basis function network, Random variable, Random-access memory, Real number, Rectifier (neural networks), Recurrent neural network, Regression analysis, Regularization (mathematics), Reinforcement learning, Restricted Boltzmann machine, Retina, Robot navigation, Robotics, Robustness (computer science), Ronald J. Williams, Rprop, Score (statistics), Secant method, Self-organizing map, Sensory neuron, Seppo Linnainmaa, Seymour Papert, Sigmoid function, Simple cell, Simulated annealing, Sleep apnea, Social network, Softmax function, Sparse distributed memory, Speech recognition, Spiking neural network, Stationary process, Statistic, Statistical classification, Statistics, Stochastic gradient descent, Structured prediction, Stuart Dreyfus, Supervised learning, Support vector machine, Synapse, System identification, Systolic array, Tensor, Tensor processing unit, Tensor product network, Teuvo Kohonen, Time delay neural network, Topology, Torsten Wiesel, Training, test, and validation sets, Travelling salesman problem, Turing machine, Types of artificial neural networks, Universal approximation theorem, Universal Turing machine, Unorganized machine, Unsupervised learning, Vanishing gradient problem, Vector (mathematics and physics), Vehicle routing problem, Visual cortex, Von Neumann architecture, Walter Pitts, Warren Sturgis McCulloch, Weighting, Wesley A. Clark, Yann LeCun, YouTube, Yu-Chi Ho, 20Q. Expand index (279 more) »

A priori and a posteriori

The Latin phrases a priori ("from the earlier") and a posteriori ("from the latter") are philosophical terms of art popularized by Immanuel Kant's Critique of Pure Reason (first published in 1781, second edition in 1787), one of the most influential works in the history of philosophy.

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Action potential

In physiology, an action potential occurs when the membrane potential of a specific axon location rapidly rises and falls: this depolarisation then causes adjacent locations to similarly depolarise.

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Activation function

In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs.

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Ad hoc

Ad hoc is a Latin phrase meaning literally "for this".

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ADALINE

ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network.

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Adaptive resonance theory

Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.

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Affine transformation

In geometry, an affine transformation, affine mapBerger, Marcel (1987), p. 38.

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Alex Graves (computer scientist)

Alex Graves is a research scientist at DeepMind.

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Alexander Dewdney

Alexander Keewatin Dewdney (born August 5, 1941, in London, Ontario) is a Canadian mathematician, computer scientist, author, filmmaker, and conspiracy theorist.

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Alexey Ivakhnenko

Alexey Ivakhnenko (Олексíй Григо́рович Іва́хненко, Алексей Григо́рьевич Іва́хненко); (30 March 1913 – 16 October 2007) was a Soviet and Ukrainian mathematician most famous for developing the Group Method of Data Handling (GMDH), a method of inductive statistical learning, for which he is sometimes referred to as the "Father of Deep Learning".

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Algorithm

In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems.

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Algorithmic trading

Algorithmic trading is a method of executing a large order (too large to fill all at once) using automated pre-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order (child orders) out to the market over time.

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Analog signal

An analog signal is any continuous signal for which the time varying feature (variable) of the signal is a representation of some other time varying quantity, i.e., analogous to another time varying signal.

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Andrew Ng

Andrew Yan-Tak Ng (born 1976) is a Chinese American computer scientist and entrepreneur.

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Andrey Kolmogorov

Andrey Nikolaevich Kolmogorov (a, 25 April 1903 – 20 October 1987) was a 20th-century Soviet mathematician who made significant contributions to the mathematics of probability theory, topology, intuitionistic logic, turbulence, classical mechanics, algorithmic information theory and computational complexity.

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Android (operating system)

Android is a mobile operating system developed by Google, based on a modified version of the Linux kernel and other open source software and designed primarily for touchscreen mobile devices such as smartphones and tablets.

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Anomaly detection

In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.

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Approximation

An approximation is anything that is similar but not exactly equal to something else.

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Arithmetic logic unit

An arithmetic logic unit (ALU) is a combinational digital electronic circuit that performs arithmetic and bitwise operations on integer binary numbers.

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Arthur E. Bryson

Arthur Earl Bryson, Jr. (born October 7, 1925) is the Pigott Professor of Engineering Emeritus at Stanford University and the "father of modern optimal control theory".

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Artificial intelligence

Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.

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Artificial life

Artificial life (often abbreviated ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry.

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Artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.

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Auditory cortex

The primary auditory cortex is the part of the temporal lobe that processes auditory information in humans and other vertebrates.

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Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.

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Automatic differentiation

In mathematics and computer algebra, automatic differentiation (AD), also called algorithmic differentiation or computational differentiation, is a set of techniques to numerically evaluate the derivative of a function specified by a computer program.

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Backgammon

Backgammon is one of the oldest known board games.

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Backpropagation

Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.

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Baidu

Baidu, Inc. (anglicized), incorporated on 18 January 2000, is a Chinese multinational technology company specializing in Internet-related services and products, and artificial intelligence, headquartered at the Baidu Campus in Beijing's Haidian District.

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Bayesian network

A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).

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Bayesian probability

Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.

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BEAM robotics

BEAM robotics (from '''B'''iology, '''E'''lectronics, '''A'''esthetics and '''M'''echanics) is a style of robotics that primarily uses simple analogue circuits, such as comparators, instead of a microprocessor in order to produce an unusually simple design.

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Bilinear map

In mathematics, a bilinear map is a function combining elements of two vector spaces to yield an element of a third vector space, and is linear in each of its arguments.

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Binary data

Binary data is data whose unit can take on only two possible states, traditionally termed 0 and +1 in accordance with the binary numeral system and Boolean algebra.

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Binary decoder

In digital electronics, a binary decoder is a combinational logic circuit that converts binary information from the n coded inputs to a maximum of 2n unique outputs.

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Bio-inspired computing

Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence.

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Biocybernetics

Biocybernetics is the application of cybernetics to biological science, composed of biological disciplines that benefit from the application of cybernetics including neurology and multicellular systems.

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Biological neuron model

A biological neuron model, also known as a spiking neuron model, is a mathematical description of the properties of certain cells in the nervous system that generate sharp electrical potentials across their cell membrane, roughly one millisecond in duration, as shown in Fig.

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Biology

Biology is the natural science that studies life and living organisms, including their physical structure, chemical composition, function, development and evolution.

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Bipartite graph

In the mathematical field of graph theory, a bipartite graph (or bigraph) is a graph whose vertices can be divided into two disjoint and independent sets U and V such that every edge connects a vertex in U to one in V. Vertex sets U and V are usually called the parts of the graph.

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Blind signal separation

Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.

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Blue Brain Project

The Blue Brain, a Swiss national brain initiative, aims to create a digital reconstruction of the brain by reverse-engineering mammalian brain circuitry.

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Brain

The brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals.

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Broyden–Fletcher–Goldfarb–Shanno algorithm

In numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems.

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Cambridge University Press

Cambridge University Press (CUP) is the publishing business of the University of Cambridge.

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Catastrophic interference

Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information.

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Central processing unit

A central processing unit (CPU) is the electronic circuitry within a computer that carries out the instructions of a computer program by performing the basic arithmetic, logical, control and input/output (I/O) operations specified by the instructions.

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Cerebellar model articulation controller

The cerebellar model arithmetic computer (CMAC) is a type of neural network based on a model of the mammalian cerebellum.

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Chain rule

In calculus, the chain rule is a formula for computing the derivative of the composition of two or more functions.

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Chess

Chess is a two-player strategy board game played on a chessboard, a checkered gameboard with 64 squares arranged in an 8×8 grid.

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Closed-form expression

In mathematics, a closed-form expression is a mathematical expression that can be evaluated in a finite number of operations.

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Cluster analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).

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CMOS

Complementary metal–oxide–semiconductor, abbreviated as CMOS, is a technology for constructing integrated circuits.

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Coastal engineering

Coastal engineering is a branch of civil engineering concerned with the specific demands posed by constructing at or near the coast, as well as the development of the coast itself.

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Cochlea

The cochlea is the part of the inner ear involved in hearing.

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Cognitive architecture

A cognitive architecture can refer to a theory about the structure of the human mind.

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Cognitive model

A cognitive model is an approximation to animal cognitive processes (predominantly human) for the purposes of comprehension and prediction.

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Cognitive science

Cognitive science is the interdisciplinary, scientific study of the mind and its processes.

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Colorectal cancer

Colorectal cancer (CRC), also known as bowel cancer and colon cancer, is the development of cancer from the colon or rectum (parts of the large intestine).

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Complex cell

Complex cells can be found in the primary visual cortex (V1), the secondary visual cortex (V2), and Brodmann area 19 (V3).

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Computational complexity theory

Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other.

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Computer architecture

In computer engineering, computer architecture is a set of rules and methods that describe the functionality, organization, and implementation of computer systems.

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Computer vision

Computer vision is a field that deals with how computers can be made for gaining high-level understanding from digital images or videos.

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Conditional probability distribution

In probability theory and statistics, given two jointly distributed random variables X and Y, the conditional probability distribution of Y given X is the probability distribution of Y when X is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value x of X as a parameter.

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Confidence interval

In statistics, a confidence interval (CI) is a type of interval estimate, computed from the statistics of the observed data, that might contain the true value of an unknown population parameter.

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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.

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Connectionism

Connectionism is an approach in the fields of cognitive science, that hopes to represent mental phenomena using artificial neural networks.

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Connectionist expert system

Connectionist expert systems are artificial neural network (ANN) based expert systems where the ANN generates inferencing rules e.g., fuzzy-multi layer perceptron where linguistic and natural form of inputs are used.

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Connectomics

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system, typically its brain or eye.

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Content-addressable memory

Content-addressable memory (CAM) is a special type of computer memory used in certain very-high-speed searching applications.

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Context-sensitive language

In formal language theory, a context-sensitive language is a language that can be defined by a context-sensitive grammar (and equivalently by a noncontracting grammar).

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Control engineering

Control engineering or control systems engineering is an engineering discipline that applies automatic control theory to design systems with desired behaviors in control environments.

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Control theory

Control theory in control systems engineering deals with the control of continuously operating dynamical systems in engineered processes and machines.

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Convex function

In mathematics, a real-valued function defined on an ''n''-dimensional interval is called convex (or convex downward or concave upward) if the line segment between any two points on the graph of the function lies above or on the graph, in a Euclidean space (or more generally a vector space) of at least two dimensions.

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Convex optimization

Convex optimization is a subfield of optimization that studies the problem of minimizing convex functions over convex sets.

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Convolution

In mathematics (and, in particular, functional analysis) convolution is a mathematical operation on two functions (f and g) to produce a third function, that is typically viewed as a modified version of one of the original functions, giving the integral of the pointwise multiplication of the two functions as a function of the amount that one of the original functions is translated.

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Convolutional neural network

In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.

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Covariance

In probability theory and statistics, covariance is a measure of the joint variability of two random variables.

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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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Cross-validation (statistics)

Cross-validation, sometimes called rotation estimation, or out-of-sample testing is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.

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Cultured neuronal network

A cultured neuronal network is a cell culture of neurons that is used as a model to study the central nervous system, especially the brain.

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Cycle (graph theory)

In graph theory, a cycle is a path of edges and vertices wherein a vertex is reachable from itself.

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DARPA

The Defense Advanced Research Projects Agency (DARPA) is an agency of the United States Department of Defense responsible for the development of emerging technologies for use by the military.

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Data compression

In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation.

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Data mining

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

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Data processing

Data processing is, generally, "the collection and manipulation of items of data to produce meaningful information." In this sense it can be considered a subset of information processing, "the change (processing) of information in any manner detectable by an observer." Data processing is distinct from word processing, which is manipulation of text specifically rather than data generally.

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Database

A database is an organized collection of data, stored and accessed electronically.

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David H. Hubel

David Hunter Hubel (February 27, 1926 – September 22, 2013) was a Canadian neurophysiologist noted for his studies of the structure and function of the visual cortex.

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David Rumelhart

David Everett Rumelhart (June 12, 1942 – March 13, 2011) was an American psychologist who made many contributions to the formal analysis of human cognition, working primarily within the frameworks of mathematical psychology, symbolic artificial intelligence, and parallel distributed processing.

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Decision-making

In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several alternative possibilities.

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Deep belief network

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.

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Deep learning

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms.

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DeepDream

DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images.

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Degrees of freedom

In many scientific fields, the degrees of freedom of a system is the number of parameters of the system that may vary independently.

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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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Differentiable neural computer

A differentiable neural computer (DNC) is a recurrent artificial neural network architecture with an autoassociative memory.

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Digital data

Digital data, in information theory and information systems, is the discrete, discontinuous representation of information or works.

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Digital morphogenesis

Digital morphogenesis is a type of generative art in which complex shape development, or morphogenesis, is enabled by computation.

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Dimensionality reduction

In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.

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Dimitri Bertsekas

Dimitri Panteli Bertsekas (b. 1942, Athens, Δημήτρης Παντελής Μπερτσεκάς) is an applied mathematician, electrical engineer, and computer scientist, and a professor at the department of Electrical Engineering and Computer Science in School of Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.

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Directed acyclic graph

In mathematics and computer science, a directed acyclic graph (DAG), is a finite directed graph with no directed cycles.

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Directed graph

In mathematics, and more specifically in graph theory, a directed graph (or digraph) is a graph that is a set of vertices connected by edges, where the edges have a direction associated with them.

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Discriminative model

Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of unobserved (target) variables y on observed variables x. Within a probabilistic framework, this is done by modeling the conditional probability distribution P(y|x), which can be used for predicting y from x. Discriminative models, as opposed to generative models, do not allow one to generate samples from the joint distribution of observed and target variables.

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Distributed computing

Distributed computing is a field of computer science that studies distributed systems.

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Donald O. Hebb

Donald Olding Hebb FRS (July 22, 1904 – August 20, 1985) was a Canadian psychologist who was influential in the area of neuropsychology, where he sought to understand how the function of neurons contributed to psychological processes such as learning.

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Dynamic programming

Dynamic programming is both a mathematical optimization method and a computer programming method.

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Earth science

Earth science or geoscience is a widely embraced term for the fields of natural science related to the planet Earth.

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Eight queens puzzle

The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other.

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Email spam

Email spam, also known as junk email, is a type of electronic spam where unsolicited messages are sent by email.

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Emergence

In philosophy, systems theory, science, and art, emergence occurs when "the whole is greater than the sum of the parts," meaning the whole has properties its parts do not have.

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Encoder

An encoder is a device, circuit, transducer, software program, algorithm or person that converts information from one format or code to another, for the purposes of standardization, speed or compression.

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Encog

Encog is a machine learning framework available for Java and.Net.

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Ensemble learning

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance that could be obtained from any of the constituent learning algorithms alone.

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Estimation theory

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component.

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Evolutionary algorithm

In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm.

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Exclusive or

Exclusive or or exclusive disjunction is a logical operation that outputs true only when inputs differ (one is true, the other is false).

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Expectation–maximization algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.

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Expert system

In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert.

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Extreme learning machine

Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need not be tuned.

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Facial recognition system

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.

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Feature (machine learning)

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed.

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Feedforward neural network

A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle.

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Finite-state machine

A finite-state machine (FSM) or finite-state automaton (FSA, plural: automata), finite automaton, or simply a state machine, is a mathematical model of computation.

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Fitness approximation

In function optimization, fitness approximation is a method for decreasing the number of fitness function evaluations to reach a target solution.

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Frank Rosenblatt

Frank Rosenblatt (July 11, 1928July 11, 1971) was an American psychologist notable in the field of artificial intelligence.

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Function approximation

In general, a function approximation problem asks us to select a function among a well-defined class that closely matches ("approximates") a target function in a task-specific way.

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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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Fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1.

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Game

A game is a structured form of play, usually undertaken for enjoyment and sometimes used as an educational tool.

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Gene expression programming

In computer programming, gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models.

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General game playing

General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully.

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General-purpose computing on graphics processing units

General-purpose computing on graphics processing units (GPGPU, rarely GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).

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Generative model

In statistical classification, including machine learning, two main approaches are called the generative approach and the discriminative approach.

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Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

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Genetic programming

In artificial intelligence, genetic programming (GP) is a technique whereby computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm (often a genetic algorithm, "GA") – it is an application of (for example) genetic algorithms where the space of solutions consists of computer programs.

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Geoffrey Hinton

Geoffrey Everest Hinton One or more of the preceding sentences incorporates text from the royalsociety.org website where: (born 6 December 1947) is a British cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.

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Geomorphology

Geomorphology (from Ancient Greek: γῆ, gê, "earth"; μορφή, morphḗ, "form"; and λόγος, lógos, "study") is the scientific study of the origin and evolution of topographic and bathymetric features created by physical, chemical or biological processes operating at or near the Earth's surface.

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Glossary of graph theory terms

This is a glossary of graph theory terms.

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Go (game)

Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent.

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Gradient

In mathematics, the gradient is a multi-variable generalization of the derivative.

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Gradient descent

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.

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Gradient-related

Gradient-related is a term used in multivariable calculus to describe a direction.

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Graph (discrete mathematics)

In mathematics, and more specifically in graph theory, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related".

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Graphical model

A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables.

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Graphics processing unit

A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device.

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Greedy algorithm

A greedy algorithm is an algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the intent of finding a global optimum.

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Group method of data handling

Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models.

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Habituation

Habituation is a form of learning in which an organism decreases or ceases its responses to a stimulus after repeated or prolonged presentations.

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Handwriting recognition

Handwriting recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.

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Hebbian theory

In neuroscience, Hebbian theory is a theory that proposes an explanation for the adaptation of neurons in the brain during the learning process.

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Henry J. Kelley

Henry J. Kelley (1926-1988) was Christopher C. Kraft Professor of Aerospace and Ocean Engineering at the Virginia Polytechnic Institute.

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Hidden Markov model

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states.

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Hierarchical Dirichlet process

In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data.

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Hierarchical temporal memory

Hierarchical temporal memory (HTM) is a technology based on a realistic biologically-constrained model of the pyramidal neuron that reflects today’s most recent neocortical research originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee.

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Holographic associative memory

Holographic Associative Memory (HAM) Is a form of information storage where two pieces of information are saved and retrieved by associating them with one another in a pattern such that any part of the pattern contains them both and either piece can be used to retrieve the other.

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Human brain

The human brain is the central organ of the human nervous system, and with the spinal cord makes up the central nervous system.

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Hydrology

Hydrology is the scientific study of the movement, distribution, and quality of water on Earth and other planets, including the water cycle, water resources and environmental watershed sustainability.

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Hypercomputation

Hypercomputation or super-Turing computation refers to models of computation that can provide outputs that are not Turing computable.

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Hyperparameter

In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.

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Identity function

Graph of the identity function on the real numbers In mathematics, an identity function, also called an identity relation or identity map or identity transformation, is a function that always returns the same value that was used as its argument.

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ImageNet

The ImageNet project is a large visual database designed for use in visual object recognition software research.

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In situ adaptive tabulation

In situ adaptive tabulation (ISAT) is an algorithm for the approximation of nonlinear relationships.

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Inference

Inferences are steps in reasoning, moving from premises to logical consequences.

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Instance-based learning

In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory.

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Integer factorization

In number theory, integer factorization is the decomposition of a composite number into a product of smaller integers.

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International Conference on Document Analysis and Recognition

The International Conference on Document Analysis and Recognition (ICDAR) is an international academic conference which is held every two years in a different city.

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James McClelland (psychologist)

James Lloyd "Jay" McClelland, FBA (born December 1, 1948) is the Lucie Stern Professor at Stanford University, where he was formerly the chair of the Psychology Department.

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Jürgen Schmidhuber

Jürgen Schmidhuber (born 17 January 1963) is a computer scientist who works in the field of artificial intelligence.

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Jeff Dean (computer scientist)

Jeffrey Adgate "Jeff" Dean (born 1968) is an American computer scientist and software engineer.

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Journal of Chemical Physics

The Journal of Chemical Physics is a scientific journal published by the American Institute of Physics that carries research papers on chemical physics.

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K-nearest neighbors algorithm

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.

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Kernel principal component analysis

In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods.

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Labeled data

Labeled data is a group of samples that have been tagged with one or more labels.

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Language model

A statistical language model is a probability distribution over sequences of words.

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Latent variable

In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).

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Learning rule

Learning rule or Learning process is a method or a mathematical logic which improves the artificial neural network's performance and usually this rule is applied repeatedly over the network.

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Levenberg–Marquardt algorithm

In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems.

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Linear classifier

In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to.

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List of Nobel laureates

The Nobel Prizes (Nobelpriset, Nobelprisen) are prizes awarded annually by the Royal Swedish Academy of Sciences, the Swedish Academy, the Karolinska Institutet, and the Norwegian Nobel Committee to individuals and organizations who make outstanding contributions in the fields of chemistry, physics, literature, peace, and physiology or medicine.

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Log probability

In computer science, a log probability is simply the logarithm of a probability.

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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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Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that is usually taken to apply to a binary dependent variable.

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Long short-term memory

Long short-term memory (LSTM) units (or blocks) are a building unit for layers of a recurrent neural network (RNN).

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Long-term memory

Long-term memory (LTM) is the stage of the Atkinson–Shiffrin memory model where informative knowledge is held indefinitely.

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Long-term potentiation

In neuroscience, long-term potentiation (LTP) is a persistent strengthening of synapses based on recent patterns of activity.

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Loss function

In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.

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Lung cancer

Lung cancer, also known as lung carcinoma, is a malignant lung tumor characterized by uncontrolled cell growth in tissues of the lung.

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Machine learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

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Machine translation

Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.

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Marginal distribution

In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset.

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Markov chain

A Markov chain is "a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event".

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Markov decision process

Markov decision processes (MDPs) provide a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

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Marvin Minsky

Marvin Lee Minsky (August 9, 1927 – January 24, 2016) was an American cognitive scientist concerned largely with research of artificial intelligence (AI), co-founder of the Massachusetts Institute of Technology's AI laboratory, and author of several texts concerning AI and philosophy.

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Mathematical optimization

In mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives.

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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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Mathematics of Control, Signals, and Systems

Mathematics of Control, Signals, and Systems is a peer-reviewed scientific journal that covers research concerned with mathematically rigorous system theoretic aspects of control and signal processing.

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Mean squared error

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and what is estimated.

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Medical diagnosis

Medical diagnosis (abbreviated Dx or DS) is the process of determining which disease or condition explains a person's symptoms and signs.

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Medicine

Medicine is the science and practice of the diagnosis, treatment, and prevention of disease.

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Merck & Co.

Merck & Company, Inc., d.b.a. Merck Sharp & Dohme (MSD) outside the United States and Canada, is an American pharmaceutical company and one of the largest pharmaceutical companies in the world.

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Minimum bounding box

In geometry, the minimum or smallest bounding or enclosing box for a point set (S) in N dimensions is the box with the smallest measure (area, volume, or hypervolume in higher dimensions) within which all the points lie.

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Minimum mean square error

In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure of estimator quality, of the fitted values of a dependent variable.

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MNIST database

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems.

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Models of neural computation

Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof.

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Molecular machine

A molecular machine, nanite, or nanomachine, refers to any discrete number of molecular components that produce quasi-mechanical movements (output) in response to specific stimuli (input).

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Motor neuron

A motor neuron (or motoneuron) is a neuron whose cell body is located in the motor cortex, brainstem or the spinal cord, and whose axon (fiber) projects to the spinal cord or outside of the spinal cord to directly or indirectly control effector organs, mainly muscles and glands.

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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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Multilayer perceptron

A multilayer perceptron (MLP) is a class of feedforward artificial neural network.

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Mutual information

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables.

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Myocyte

A myocyte (also known as a muscle cell) is the type of cell found in muscle tissue.

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Naive Bayes spam filtering

Naive Bayes classifiers are a popular statistical technique of e-mail filtering.

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Nathaniel Rochester (computer scientist)

Nathaniel Rochester (January 14, 1919 – June 8, 2001) designed the IBM 701, wrote the first assembler and participated in the founding of the field of artificial intelligence.

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National Science Foundation

The National Science Foundation (NSF) is a United States government agency that supports fundamental research and education in all the non-medical fields of science and engineering.

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Natural language processing

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

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Natural resource management

Natural resource management refers to the management of natural resources such as land, water, soil, plants and animals, with a particular focus on how management affects the quality of life for both present and future generations (stewardship).

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Neocognitron

The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in the 1980s.

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Neural circuit

A neural circuit, is a population of neurons interconnected by synapses to carry out a specific function when activated.

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Neural coding

Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble.

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Neural Computation (journal)

Neural Computation is a monthly peer-reviewed scientific journal covering all aspects of neural computation, including modeling the brain and the design and construction of neurally-inspired information processing systems.

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Neural gas

Neural gas is an artificial neural network, inspired by the self-organizing map and introduced in 1991 by Thomas Martinetz and Klaus Schulten.

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Neural machine translation

Neural machine translation (NMT) is an approach to machine translation that uses a large artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

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Neural network software

Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning.

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Neuroevolution

Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, topology and rules.

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Neuromorphic engineering

Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

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Neuron

A neuron, also known as a neurone (British spelling) and nerve cell, is an electrically excitable cell that receives, processes, and transmits information through electrical and chemical signals.

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Neuroplasticity

Neuroplasticity, also known as brain plasticity and neural plasticity, is the ability of the brain to change throughout an individual's life, e.g., brain activity associated with a given function can be transferred to a different location, the proportion of grey matter can change, and synapses may strengthen or weaken over time.

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Neuroscience

Neuroscience (or neurobiology) is the scientific study of the nervous system.

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Ni1000

The Ni1000 is an artificial neural network chip developed by Nestor Corporation.

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Nonlinear system identification

System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs.

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Nonparametric statistics

Nonparametric statistics is the branch of statistics that is not based solely on parameterized families of probability distributions (common examples of parameters are the mean and variance).

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Norm (mathematics)

In linear algebra, functional analysis, and related areas of mathematics, a norm is a function that assigns a strictly positive length or size to each vector in a vector space—save for the zero vector, which is assigned a length of zero.

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Normal distribution

In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.

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Numerical control

Computer numerical control (CNC) is the automation of machine tools by means of computers executing pre-programmed sequences of machine control commands.

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Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update our best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.

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Optical neural network

An optical neural network is a physical implementation of an artificial neural network with optical components.

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Outline of machine learning

The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of computer sciencehttp://www.britannica.com/EBchecked/topic/1116194/machine-learning (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.

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Parallel constraint satisfaction processes

Parallel constraint satisfaction processes (PCSP) is a model that integrates the fastest growing research areas in the study of the mind; Connectionism, neural networks, and parallel distributed processing models.

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Parameter

A parameter (from the Ancient Greek παρά, para: "beside", "subsidiary"; and μέτρον, metron: "measure"), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when identifying the system, or when evaluating its performance, status, condition, etc.

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Particle swarm optimization

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

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Pattern recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning.

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Paul Werbos

Paul J. Werbos (born 1947) is a scientist best known for his 1974 Harvard University Ph.D. thesis, which first described the process of training artificial neural networks through backpropagation of errors.

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Perceptron

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).

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Pointer (computer programming)

In computer science, a pointer is a programming language object that stores the memory address of another value located in computer memory.

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Poker

Poker is a family of card games that combines gambling, strategy, and skill.

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Posterior probability

In Bayesian statistics, the posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account.

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Predictive modelling

Predictive modelling uses statistics to predict outcomes.

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Principal component analysis

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

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Prior probability

In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account.

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Probability density function

In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function, whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.

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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 mass function

In probability and statistics, a probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.

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Process control

Automatic process control in continuous production processes is a combination of control engineering and chemical engineering disciplines that uses industrial control systems to achieve a production level of consistency, economy and safety which could not be achieved purely by human manual control.

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Processor register

In computer architecture, a processor register is a quickly accessible location available to a computer's central processing unit (CPU).

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Prostate cancer

Prostate cancer is the development of cancer in the prostate, a gland in the male reproductive system.

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Prosthesis

In medicine, a prosthesis (plural: prostheses; from Ancient Greek prosthesis, "addition, application, attachment") is an artificial device that replaces a missing body part, which may be lost through trauma, disease, or congenital conditions.

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QR decomposition

In linear algebra, a QR decomposition (also called a QR factorization) of a matrix is a decomposition of a matrix A into a product A.

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Quantum chemistry

Quantum chemistry is a branch of chemistry whose primary focus is the application of quantum mechanics in physical models and experiments of chemical systems.

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Question answering

Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.

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Radial basis function network

In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions.

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Random variable

In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.

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Random-access memory

Random-access memory (RAM) is a form of computer data storage that stores data and machine code currently being used.

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Real number

In mathematics, a real number is a value of a continuous quantity that can represent a distance along a line.

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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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Recurrent neural network

A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence.

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Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.

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Regularization (mathematics)

In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.

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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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Restricted Boltzmann machine

A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

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Retina

The retina is the innermost, light-sensitive "coat", or layer, of shell tissue of the eye of most vertebrates and some molluscs.

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Robot navigation

For any mobile device, the ability to navigate in its environment is important.

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Robotics

Robotics is an interdisciplinary branch of engineering and science that includes mechanical engineering, electronics engineering, computer science, and others.

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Robustness (computer science)

In computer science, robustness is the ability of a computer system to cope with errors during execution1990.

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Ronald J. Williams

Ronald J. Williams is professor of computer science at Northeastern University, and one of the pioneers of neural networks.

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Rprop

Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks.

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Score (statistics)

In statistics, the score, score function, efficient score or informant indicates how sensitive a likelihood function \mathcal L(\theta; X) is to its parameter \theta.

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Secant method

In numerical analysis, the secant method is a root-finding algorithm that uses a succession of roots of secant lines to better approximate a root of a function f. The secant method can be thought of as a finite-difference approximation of Newton's method.

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Self-organizing map

A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.

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Sensory neuron

Sensory neurons also known as afferent neurons are neurons that convert a specific type of stimulus, via their receptors, into action potentials or graded potentials.

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Seppo Linnainmaa

Seppo Linnainmaa (born 1945 in Pori, Finland) is a Finnish mathematician and computer scientist.

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Seymour Papert

Seymour Aubrey Papert (February 29, 1928 – July 31, 2016) was a South African-born American mathematician, computer scientist, and educator, who spent most of his career teaching and researching at MIT.

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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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Simple cell

A simple cell in the primary visual cortex is a cell that responds primarily to oriented edges and gratings (bars of particular orientations).

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Simulated annealing

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.

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Sleep apnea

Sleep apnea, also spelled sleep apnoea, is a sleep disorder characterized by pauses in breathing or periods of shallow breathing during sleep.

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Social network

A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors.

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Softmax function

In mathematics, the softmax function, or normalized exponential function, is a generalization of the logistic function that "squashes" a -dimensional vector \mathbf of arbitrary real values to a -dimensional vector \sigma(\mathbf) of real values, where each entry is in the range (0, 1, and all the entries add up to 1. The function is given by In probability theory, the output of the softmax function can be used to represent a categorical distribution – that is, a probability distribution over different possible outcomes. In fact, it is the gradient-log-normalizer of the categorical probability distribution. The softmax function is also the gradient of the LogSumExp function. The softmax function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), multiclass linear discriminant analysis, naive Bayes classifiers, and artificial neural networks. Specifically, in multinomial logistic regression and linear discriminant analysis, the input to the function is the result of distinct linear functions, and the predicted probability for the 'th class given a sample vector and a weighting vector is: This can be seen as the composition of linear functions \mathbf \mapsto \mathbf^\mathsf\mathbf_1, \ldots, \mathbf \mapsto \mathbf^\mathsf\mathbf_K and the softmax function (where \mathbf^\mathsf\mathbf denotes the inner product of \mathbf and \mathbf). The operation is equivalent to applying a linear operator defined by \mathbf to vectors \mathbf, thus transforming the original, probably highly-dimensional, input to vectors in a -dimensional space \mathbb^K.

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Sparse distributed memory

Sparse distributed memory (SDM) is a mathematical model of human long-term memory introduced by Pentti Kanerva in 1988 while he was at NASA Ames Research Center.

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Speech recognition

Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.

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Spiking neural network

Spiking neural networks (SNNs) fall into the third generation of artificial neural network models, increasing the level of realism in a neural simulation.

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Stationary process

In mathematics and statistics, a stationary process (a strict/strictly stationary process or strong/strongly stationary process) is a stochastic process whose unconditional joint probability distribution does not change when shifted in time.

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Statistic

A statistic (singular) or sample statistic is a single measure of some attribute of a sample (e.g. its arithmetic mean value).

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Statistical classification

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

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Statistics

Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.

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Stochastic gradient descent

Stochastic gradient descent (often shortened to SGD), also known as incremental gradient descent, is an iterative method for optimizing a differentiable objective function, a stochastic approximation of gradient descent optimization.

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Structured prediction

Structured prediction or structured (output) learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than scalar discrete or real values.

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Stuart Dreyfus

A native of Terre Haute, Indiana, Stuart E. Dreyfus is Professor Emeritus at University of California, Berkeley in the Industrial Engineering and Operations Research Department.

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Supervised learning

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

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Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

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Synapse

In the nervous system, a synapse is a structure that permits a neuron (or nerve cell) to pass an electrical or chemical signal to another neuron or to the target efferent cell.

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System identification

The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data.

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Systolic array

In parallel computer architectures, a systolic array is a homogeneous network of tightly coupled data processing units (DPUs) called cells or nodes.

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Tensor

In mathematics, tensors are geometric objects that describe linear relations between geometric vectors, scalars, and other tensors.

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Tensor processing unit

A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning.

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Tensor product network

A tensor product network, in artificial neural networks, is a network that exploits the properties of tensors to model associative concepts such as variable assignment.

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Teuvo Kohonen

Teuvo Kalevi Kohonen (born July 11, 1934) is a prominent Finnish academic (Dr. Eng.) and researcher.

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Time delay neural network

Time delay neural network (TDNN) Alexander Waibel, Tashiyuki Hanazawa, Geoffrey Hinton, Kiyohito Shikano, Kevin J. Lang, Phoneme Recognition Using Time-Delay Neural Networks, IEEE Transactions on Acoustics, Speech and Signal Processing, Volume 37, No.

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Topology

In mathematics, topology (from the Greek τόπος, place, and λόγος, study) is concerned with the properties of space that are preserved under continuous deformations, such as stretching, crumpling and bending, but not tearing or gluing.

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Torsten Wiesel

Torsten Nils Wiesel (born 3 June 1924) is a Swedish neurophysiologist.

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Training, test, and validation sets

In machine learning, the study and construction of algorithms that can learn from and make predictions on data is a common task.

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Travelling salesman problem

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 and returns to the origin city?" It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.

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Turing machine

A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules.

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Types of artificial neural networks

There are many types of artificial neural networks (ANN).

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Universal approximation theorem

In the mathematical theory of artificial neural networks, the universal approximation theorem states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of '''R'''n, under mild assumptions on the activation function.

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Universal Turing machine

In computer science, a universal Turing machine (UTM) is a Turing machine that can simulate an arbitrary Turing machine on arbitrary input.

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Unorganized machine

An unorganized machine is a concept mentioned in a 1948 report in which Alan Turing suggested that the infant human cortex was what he called an "unorganised machine".

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Unsupervised learning

Unsupervised machine learning is the machine learning task of inferring a function that describes the structure of "unlabeled" data (i.e. data that has not been classified or categorized).

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Vanishing gradient problem

In machine learning, the vanishing gradient problem is a difficulty found in training artificial neural networks with gradient-based learning methods and backpropagation.

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Vector (mathematics and physics)

When used without any further description, vector usually refers either to.

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Vehicle routing problem

The vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem which asks "What is the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers?".

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Visual cortex

The visual cortex of the brain is a part of the cerebral cortex that processes visual information.

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Von Neumann architecture

The von Neumann architecture, which is also known as the von Neumann model and Princeton architecture, is a computer architecture based on the 1945 description by the mathematician and physicist John von Neumann and others in the First Draft of a Report on the EDVAC.

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Walter Pitts

Walter Harry Pitts, Jr. (23 April 1923 – 14 May 1969) was a logician who worked in the field of computational neuroscience.

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Warren Sturgis McCulloch

Warren Sturgis McCulloch (November 16, 1898 – September 24, 1969) was an American neurophysiologist and cybernetician, known for his work on the foundation for certain brain theories and his contribution to the cybernetics movement.

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Weighting

The process of weighting involves emphasizing the contribution of some aspects of a phenomenon (or of a set of data) to a final effect or result, giving them more weight in the analysis.

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Wesley A. Clark

Wesley Allison Clark (April 10, 1927 – February 22, 2016) was an American physicist who is credited for designing the first modern personal computer.

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Yann LeCun

Yann LeCun (born 1960) is a computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience.

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YouTube

YouTube is an American video-sharing website headquartered in San Bruno, California.

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Yu-Chi Ho

Yu-Chi "Larry" Ho (born March 1, 1934) is a Chinese-American mathematician, control theorist, and a professor at the School of Engineering and Applied Sciences, Harvard University.

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20Q

20Q is a computerized game of twenty questions that began as a test in artificial intelligence (AI).

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References

[1] https://en.wikipedia.org/wiki/Artificial_neural_network

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