Which factors affecting the back propagation explain it
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Neural systems have been utilized successfully in various applications. A large portion of these applications has utilized the backpropagation calculation as the learning algorithm.
One of the real issues with this calculation is that its joining time is typical since a long time ago the preparation set must be displayed ordinarily to the system. The learning rate must be chosen precisely.
On the off chance that the learning rate is too low, the system will take more time to join. Then again, if the learning rate is too high, the system might be temperamental and may never merge.
Up to now, fashioners of neural system applications needed to locate a suitable learning rate for their systems by experimentation.
One of the real issues with this calculation is that its joining time is typical since a long time ago the preparation set must be displayed ordinarily to the system. The learning rate must be chosen precisely.
On the off chance that the learning rate is too low, the system will take more time to join. Then again, if the learning rate is too high, the system might be temperamental and may never merge.
Up to now, fashioners of neural system applications needed to locate a suitable learning rate for their systems by experimentation.
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