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the reason is different. network redundancy.
And let's work out what network redundancy is?)
A fitting essentially.
Fitting is when the sample is too small. Network redundancy has little effect on fitting.
Let's work out what redundancy means, shall we?)
There's no need to, it's already clear that the number of layers and weights is very large.
But I will add to the question of fitting... A redundant network is like a system of 4 equations with two unknowns. Either the network will trivially learn all the data, or the solution will be correct but unstable.
A fitting in essence.
This is understandable. How do you determine the necessary sufficiency of a network? If there is redundancy, is there sufficiency?
Either the network will learn all the data.
How much data can the network learn?
Are we talking about all networks or MLPs?
This is understandable. How do you determine the necessary sufficiency of a network? If there is redundancy, is there sufficiency?