Neural networks

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  • Neural networks

    1. 1. AGENDAWhat is a Neural NetworkHistory of Neural NetworksTypes of learning for Neural NetworksWhere are Neural Networks applicableNeural Networks vs Conventional Computers
    2. 2. AGENDAWhat is a Neural NetworkHistory of Neural NetworksTypes of learning for Neural NetworksWhere are Neural Networks applicableNeural Networks vs Conventional Computers
    3. 3. AGENDAWhat is a Neural Network HistoryThe term  neural network  was traditionally Typesused to refer to a network or circuit Whereof biological neurons. NeuralAn artificial neural network, is a mathematicalmodel or computational model.What has attracted the most interest in neuralnetworks is the possibility of learning. Given aspecific task to solve.
    4. 4. AGENDAHistory of Neural Networks What1940s - The first artificial neuron wasThe term  neural network  was traditionally Typesproduced by  the to a network or Warrenused to refer neurophysiologist circuit WhereMcCulloch and the logician Walter Pits.of biological neurons. Neural1950s - The perceptron  by FrankAn artificial neural network, is a mathematicalRosenblatt.model or computational model.1980s - D.O.D, Boltzmann machines,in neuralWhat has attracted the most interestHopfield nets, competitive learning models, anetworks is the possibility of learning. Givenmultilayer networks.specific task to solve.
    5. 5. AGENDATypes of learning for Neural Networks Supervised learning What1940s - wants firstinfer  thewasneuronimplied The user The to  artificialThe term  neural network  mapping was traditionally Historyproduced by  the costa networkrelated to the by the to refer to function is or Warrenused data, the neurophysiologist circuit WhereMcCulloch and the logician Walterand the data mismatch between our mapping Pits.of biological neurons. Neural and it implicitly contains prior knowledge1950s the problem network, is a mathematical about - The perceptron  by FrankAn artificial neural domain.Rosenblatt.model or computational model. Unsupervised learning1980s - D.O.D, Boltzmannthe cost function to Some data  is given and machines,What has attracted the most interest in neuralnetworks is the The cost function is dependent be minimized, possibility of learning. GivenHopfield nets, competitive learning models, amultilayer networks. the implicit properties of on the task and onspecific task to solve. our model, its parameters and the observed variables.
    6. 6. AGENDATypes of learning for Neural Networks Reinforcement learning What1940s - The first not given, neuron wasThe data are usually artificial but generated The term  neural network  was traditionally Historyproduced aby  n t sto i n t e networks or iWarrenuseda n refer neurophysiologist tcircuit by to g e the a raction w h the WhereMcCulloch andAt each point in time  the agent environment. the logician Walter Pits.of biological neurons. Neural performs an action  and the environment1950sr- The perceptron  by Frank   a n d a nAn artificial s a n network,aisi o n  g e n e a t e neural o b s e r v t a mathematical instantaneous cost.Rosenblatt.model or computational model. Learning algorithms1980s - D.O.D, Boltzmann machines,in neuralWhat has attracted the most interestHopfield nets, competitive learning models, a Training a neural network model essentiallynetworks is the possibility of learning. Givenmultilayer networks.specific task to solve. model from the set of means selecting one allowed model that minimizes the cost criterion.
    7. 7. AGENDAWhere are Neural Networks applicable Reinforcement learning What1940s - The first not given, neuron wasThe data are usually artificial but generated The term  neural network  was traditionally Historyproduced aby  n t sto i n t e networks or iWarrenuseda n refer neurophysiologist tcircuit by to g e the a raction w h the TypesMcCulloch andAt each point in time  the agent environment. the logician Walter Pits.of biological neurons. •Investment analysis and the environment Neural performs an action  •Robotics •e n e r- The perceptrone by Frank   a n d a n Credit Evaluationo b s r v a t i o n 1950s a t e s a n network, is a mathematical g •MedicineAn artificial neural •Signature analysis •Weather instantaneous cost.Rosenblatt.model or computational model. •Marketing •Intelligent Searching •Monitoring Learning algorithms •Games1980s - D.O.D, Boltzmann machines,in neuralWhat has attracted the most interest •Staff scheduling network modelmodels,Hopfield nets, competitive learning essentially Training a neuralnetworks is the possibility of learning. Given amultilayer networks.specific task to solve. model from the set of means selecting one allowed model that minimizes the cost criterion.
    8. 8. AGENDANeural Networks vs Conventional Computers Reinforcement learning What1940s - The first not given, neuron wasThe data are usually artificial but generated The term  neural network  was traditionally Historyproduced aby  n t sto i n t e networks or iWarrenuseda n refer neurophysiologist tcircuit by to g e the a raction w h the TypesMcCulloch andAt each point in time  the agent environment. the logician Walter Pits.of biological neurons. Where performs an action  and the environmentWhat do you think?1950sr- The perceptron  by Frank   a n d a nAn artificial s a n network,aisi o n  g e n e a t e neural o b s e r v t a mathematical instantaneous cost.Rosenblatt.model or computational model. Learning algorithms1980s - D.O.D, Boltzmann machines,in neuralWhat has attracted the most interestHopfield nets, competitive learning models, a Training a neural network model essentiallynetworks is the possibility of learning. Givenmultilayer networks.specific task to solve. model from the set of means selecting one allowed model that minimizes the cost criterion.

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