Process of improving the accuracy of a neural network is called
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●Artificial Neural Network (ANN) is a form of connectionism, system computing inspired by biological nervous network.
●which constitute the animal brain. Such systems learn (to improve performance), to work by considering examples, in general, for instance, in image recognition, without work-specific programming, they can learn to identify such images, for example Cats are included by analyzing the pictures, which have not been manually labeled as "cat" or "no cat" and in other images A. You can use the analytical results to identify cats. They used the most applications in difficult applications to express in a traditional computer algorithm using rule-based programming.
An ANN is based on a collection of connected units called artificial neurons (analogous to the biological brain). Between neurons, each connection (synapse) can transmit a signal to a neuron. Received (postcinaptic) neuron can process signals (PRO) and then indicate downstream neurons associated with it. Neurons can have a state, usually represented by actual numbers between 0 and 1. Neurons and surgery can also have a weight that differs in learning form, which can increase or decrease the strength of the signal which sends it downward. In addition, they may have a threshold as if the total signal is below (or above), then the downstream signal is sent to that level
Typically, neurons are organized in the layers. Different layers can make different types of changes on their input. Signals are taken from the first (input) to the last (output) layer, possibly in the artificial network with many hidden layers after carrying the layers several times, early layers of ancient cells (such as men in the eyes, iris, eyelids, etc.) ). And their production is fed further to deeper layers, which are more generalized (like eyes, mouth) .... And so on until the last layer does not exhibit complex object recognition (like face)
The basic goal of the neural network's approach was to solve problems in the same way what the human brain would be. Over time, it is focused on matching specific mental abilities, thereby deviating from biology in backpropage, or passing information in the reverse direction and adjusting the network to reflect that information.
Neural networks have been used in a variety of functions, including computer vision, speech recognition, machine translation, social network filtering, board and video games, medical diagnosis, and many other domains.
■I HOPE ITS HELP■
●Artificial Neural Network (ANN) is a form of connectionism, system computing inspired by biological nervous network.
●which constitute the animal brain. Such systems learn (to improve performance), to work by considering examples, in general, for instance, in image recognition, without work-specific programming, they can learn to identify such images, for example Cats are included by analyzing the pictures, which have not been manually labeled as "cat" or "no cat" and in other images A. You can use the analytical results to identify cats. They used the most applications in difficult applications to express in a traditional computer algorithm using rule-based programming.
An ANN is based on a collection of connected units called artificial neurons (analogous to the biological brain). Between neurons, each connection (synapse) can transmit a signal to a neuron. Received (postcinaptic) neuron can process signals (PRO) and then indicate downstream neurons associated with it. Neurons can have a state, usually represented by actual numbers between 0 and 1. Neurons and surgery can also have a weight that differs in learning form, which can increase or decrease the strength of the signal which sends it downward. In addition, they may have a threshold as if the total signal is below (or above), then the downstream signal is sent to that level
Typically, neurons are organized in the layers. Different layers can make different types of changes on their input. Signals are taken from the first (input) to the last (output) layer, possibly in the artificial network with many hidden layers after carrying the layers several times, early layers of ancient cells (such as men in the eyes, iris, eyelids, etc.) ). And their production is fed further to deeper layers, which are more generalized (like eyes, mouth) .... And so on until the last layer does not exhibit complex object recognition (like face)
The basic goal of the neural network's approach was to solve problems in the same way what the human brain would be. Over time, it is focused on matching specific mental abilities, thereby deviating from biology in backpropage, or passing information in the reverse direction and adjusting the network to reflect that information.
Neural networks have been used in a variety of functions, including computer vision, speech recognition, machine translation, social network filtering, board and video games, medical diagnosis, and many other domains.
■I HOPE ITS HELP■
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A neural network's accuracy training is done with the help of error checking. When the error between the desired output and the expected output is below some threshold value, the desired accuracy is said to be achieved.This process is called cross validation. This process is carried out by several techniques. Cross Validation is one of the most important concepts in data modeling.
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