Ordinary gradient descent can get stuck at an area bare minimum as opposed to a global least, resulting in a subpar network. In usual gradient descent, we take all our rows and plug them to the similar neural network, Have a look at the weights, after which you can modify them.In forward propagation, data is entered into your enter layer and propag
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