Robot de tranzacționare bazat pe rețea neuronală, Pot câștiga bani investind în bitcoin roboti de tranzacționare bursă
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Warren McCulloch and Walter Pitts  opened the subject by creating a computational model for neural networks. Hebb  created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning.
Farley and Wesley A. Clark  first used computational machines, then called "calculators", to simulate a Hebbian network. Rosenblatt  created the perceptron.
InSeppo Linnainmaa published the general method for automatic differentiation AD of discrete connected networks of nested differentiable functions. Inhe applied Linnainmaa's AD method to neural networks in the way that became widely used.
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This provided more processing power for the development of practical artificial neural networks in the s. InNg and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images.
This section may be confusing or unclear to readers. Please help us clarify the section.
There might be a discussion about this on the talk page. April Further information: Mathematics of artificial neural networks Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with.
They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others.
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The network forms a directedweighted graph. Each neuron is robot de tranzacționare bazat pe rețea neuronală node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one robot de tranzacționare bazat pe rețea neuronală influence on another. Each artificial neuron has inputs and produce a single output which can be sent to multiple other neurons.
The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons.
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The outputs of the final output neurons of the neural net accomplish the task, such as recognizing an object in an image. To find the output of the neuron, first we take the weighted sum of all the inputs, weighted by the weights of the connections from the inputs to the neuron. We add a bias term to this sum. This weighted sum is sometimes called the activation. This weighted sum is then passed through a usually nonlinear activation function to produce the output.
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The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image. Each connection is assigned a weight that represents its relative importance. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers.
Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be fully connected, with every neuron in one layer connecting to every neuron in the next layer.
They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer.
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The values of parameters are derived via learning. Examples of hyperparameters include learning ratethe number of hidden layers and batch size. For example, the size of some layers can depend on the overall number of layers.
Learning[ edit ] This section includes a list of referencesrelated reading or external linksbut its sources remain unclear because it lacks inline citations.
Please help to improve this section by introducing more precise citations.
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August See also: Mathematical optimizationEstimation theoryand Machine learning Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights and optional thresholds of the network to improve the accuracy of the result. This is done by minimizing the observed errors. Learning is complete when cum să tranzacționați opțiuni pentru video additional observations does not usefully reduce the error rate.
Even after learning, the error rate typically does not reach 0. If after learning, the error rate is too high, the network typically must be redesigned.
Practically this is done by defining a cost function that is evaluated periodically during learning. As long as its output continues to decline, learning continues. The cost is frequently defined as a statistic whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between the output almost certainly a cat and the correct answer cat is small.
Learning attempts to reduce the total of the differences across the observations. Cele mai bune opțiuni binare din clasamentul mondial 2020 rate[ edit ] The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy.
Optimizations such as Quickprop are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability.
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In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate. A momentum close to 0 emphasizes the gradient, while a value close to 1 emphasizes the last change. Cost function[ edit ] While it is possible to define a cost function ad hocfrequently the choice is determined by the function's desirable properties such as convexity or because it arises from the model e.
Main article: Backpropagation Backpropagation is a method to adjust the connection weights to compensate for each error found during learning.
The error amount is effectively divided among the connections. Technically, backprop calculates the gradient the derivative of the cost function associated with a given state with respect to the weights.
The weight updates can be done via stochastic gradient descent or other methods, such as Extreme Learning Machines "No-prop" networks,  training without backtracking,  "weightless" networks,   and non-connectionist neural networks.
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Learning paradigms[ edit ] This section includes a list of referencesrelated reading or external linksbut its sources remain unclear because it lacks inline citations. August
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