16 relations: Artificial neural network, Atari 2600, Backgammon, Deep learning, Expected value, Function approximation, Game theory, Google DeepMind, Markov decision process, Peter Norvig, Prentice Hall, Probably approximately correct learning, Reinforcement learning, State-Action-Reward-State-Action, Stuart J. Russell, Temporal difference learning.
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.
The Atari 2600, or Atari VCS before 1982, is a home video game console released on September 11, 1977 by Atari, Inc.
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Backgammon is one of the oldest board games for two players.
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Deep learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise, composed of multiple non-linear transformations.
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In probability theory, the expected value of a random variable is intuitively the long-run average value of repetitions of the experiment it represents.
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The need for function approximations arises in many branches of applied mathematics, and computer science in particular.
Game theory is the study of strategic decision-making.
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Google DeepMind is a British artificial intelligence company.
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Markov decision processes (MDPs), named after Andrey Markov, provide a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.
Peter Norvig (born December 14, 1956) is an American computer scientist.
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Prentice Hall is a major educational publisher owned by Pearson PLC.
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In computational learning theory, probably approximately correct learning (PAC learning) is a framework for mathematical analysis of machine learning.
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
State-Action-Reward-State-Action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning.
Stuart Jonathan Russell (born 1962) is a computer scientist known for his contributions to artificial intelligence.
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Temporal difference (TD) learning is a prediction-based machine learning method.