Faster access than browser!Similarities between Artificial neural network and Machine learning
Artificial neural network and Machine learning have 46 things in common (in Unionpedia): ADALINE, Algorithm, Andrew Ng, Artificial intelligence, Artificial neuron, Backpropagation, Cluster analysis, Computer vision, Connectionism, Cross-validation (statistics), Data mining, David Rumelhart, Deep learning, Dimensionality reduction, Directed acyclic graph, Evolutionary algorithm, Expert system, General game playing, Genetic algorithm, Geoffrey Hinton, Graphical model, Graphics processing unit, Handwriting recognition, Inference, Loss function, Mathematical optimization, Natural language processing, Neural circuit, Neural coding, Neural Computation (journal), ..., Outline of machine learning, Pattern recognition, Perceptron, Predictive modelling, Principal component analysis, Probability distribution, Random variable, Regression analysis, Reinforcement learning, Speech recognition, Statistical classification, Statistics, Supervised learning, Tensor, Training, test, and validation sets, Unsupervised learning. Expand index (16 more) »
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.
ADALINE and Artificial neural network · ADALINE and Machine learning ·
Algorithm
In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems.
Algorithm and Artificial neural network · Algorithm and Machine learning ·
Andrew Ng
Andrew Yan-Tak Ng (born 1976) is a Chinese American computer scientist and entrepreneur.
Andrew Ng and Artificial neural network · Andrew Ng and Machine learning ·
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.
Artificial intelligence and Artificial neural network · Artificial intelligence and Machine learning ·
Artificial neuron
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.
Artificial neural network and Artificial neuron · Artificial neuron and Machine learning ·
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.
Artificial neural network and Backpropagation · Backpropagation and Machine learning ·
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).
Artificial neural network and Cluster analysis · Cluster analysis and Machine learning ·
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.
Artificial neural network and Computer vision · Computer vision and Machine learning ·
Connectionism
Connectionism is an approach in the fields of cognitive science, that hopes to represent mental phenomena using artificial neural networks.
Artificial neural network and Connectionism · Connectionism and Machine learning ·
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.
Artificial neural network and Cross-validation (statistics) · Cross-validation (statistics) and Machine learning ·
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.
Artificial neural network and Data mining · Data mining and Machine learning ·
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.
Artificial neural network and David Rumelhart · David Rumelhart and Machine learning ·
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.
Artificial neural network and Deep learning · Deep learning and Machine learning ·
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.
Artificial neural network and Dimensionality reduction · Dimensionality reduction and Machine learning ·
Directed acyclic graph
In mathematics and computer science, a directed acyclic graph (DAG), is a finite directed graph with no directed cycles.
Artificial neural network and Directed acyclic graph · Directed acyclic graph and Machine learning ·
Evolutionary algorithm
In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm.
Artificial neural network and Evolutionary algorithm · Evolutionary algorithm and Machine learning ·
Expert system
In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert.
Artificial neural network and Expert system · Expert system and Machine learning ·
General game playing
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully.
Artificial neural network and General game playing · General game playing and Machine learning ·
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).
Artificial neural network and Genetic algorithm · Genetic algorithm and Machine learning ·
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.
Artificial neural network and Geoffrey Hinton · Geoffrey Hinton and Machine learning ·
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.
Artificial neural network and Graphical model · Graphical model and Machine learning ·
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.
Artificial neural network and Graphics processing unit · Graphics processing unit and Machine learning ·
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.
Artificial neural network and Handwriting recognition · Handwriting recognition and Machine learning ·
Inference
Inferences are steps in reasoning, moving from premises to logical consequences.
Artificial neural network and Inference · Inference and Machine learning ·
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.
Artificial neural network and Loss function · Loss function and Machine learning ·
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.
Artificial neural network and Mathematical optimization · Machine learning and Mathematical optimization ·
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.
Artificial neural network and Natural language processing · Machine learning and Natural language processing ·
Neural circuit
A neural circuit, is a population of neurons interconnected by synapses to carry out a specific function when activated.
Artificial neural network and Neural circuit · Machine learning and Neural circuit ·
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.
Artificial neural network and Neural coding · Machine learning and Neural coding ·
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.
Artificial neural network and Neural Computation (journal) · Machine learning and Neural Computation (journal) ·
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.
Artificial neural network and Outline of machine learning · Machine learning and Outline of machine learning ·
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.
Artificial neural network and Pattern recognition · Machine learning and Pattern recognition ·
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).
Artificial neural network and Perceptron · Machine learning and Perceptron ·
Predictive modelling
Predictive modelling uses statistics to predict outcomes.
Artificial neural network and Predictive modelling · Machine learning and Predictive modelling ·
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.
Artificial neural network and Principal component analysis · Machine learning and Principal component analysis ·
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.
Artificial neural network and Probability distribution · Machine learning and Probability distribution ·
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.
Artificial neural network and Random variable · Machine learning and Random variable ·
Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.
Artificial neural network and Regression analysis · Machine learning and Regression analysis ·
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.
Artificial neural network and Reinforcement learning · Machine learning and Reinforcement learning ·
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.
Artificial neural network and Speech recognition · Machine learning and Speech recognition ·
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.
Artificial neural network and Statistical classification · Machine learning and Statistical classification ·
Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
Artificial neural network and Statistics · Machine learning and Statistics ·
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.
Artificial neural network and Supervised learning · Machine learning and Supervised learning ·
Tensor
In mathematics, tensors are geometric objects that describe linear relations between geometric vectors, scalars, and other tensors.
Artificial neural network and Tensor · Machine learning and Tensor ·
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.
Artificial neural network and Training, test, and validation sets · Machine learning and Training, test, and validation sets ·
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).
Artificial neural network and Unsupervised learning · Machine learning and Unsupervised learning ·
The list above answers the following questions
- What Artificial neural network and Machine learning have in common
- What are the similarities between Artificial neural network and Machine learning
Artificial neural network and Machine learning Comparison
Artificial neural network has 329 relations, while Machine learning has 254. As they have in common 46, the Jaccard index is 7.89% = 46 / (329 + 254).
References
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