24 relations: Angular velocity, Artificial neural network, Atari 2600, Backpropagation, Convolution, Convolutional neural network, Deep learning, DeepMind, Deterministic system, Expected value, Function approximation, Game theory, Intelligent agent, Machine learning, Markov decision process, Peter Norvig, Prentice Hall, Probably approximately correct learning, Pseudocode, Reinforcement learning, State–action–reward–state–action, Stochastic process, Stuart J. Russell, Temporal difference learning.
In physics, the angular velocity of a particle is the rate at which it rotates around a chosen center point: that is, the time rate of change of its angular displacement relative to the origin.
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
The Atari 2600 (or Atari Video Computer System before November 1982) is a home video game console from Atari, Inc. Released on September 11, 1977, it is credited with popularizing the use of microprocessor-based hardware and games contained on ROM cartridges, a format first used with the Fairchild Channel F in 1976.
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.
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.
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.
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.
DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010.
In mathematics, computer science and physics, a deterministic system is a system in which no randomness is involved in the development of future states of the system.
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
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.
Game theory is "the study of mathematical models of conflict and cooperation between intelligent rational decision-makers".
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics).
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.
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.
Peter Norvig (born December 14, 1956) is an American computer scientist.
Prentice Hall is a major educational publisher owned by Pearson plc.
In computational learning theory, probably approximately correct learning (PAC learning) is a framework for mathematical analysis of machine learning.
Pseudocode is an informal high-level description of the operating principle of a computer program or other algorithm.
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.
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.
--> In probability theory and related fields, a stochastic or random process is a mathematical object usually defined as a collection of random variables.
Stuart Jonathan Russell (born 1962) is a computer scientist known for his contributions to artificial intelligence.
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function.