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Are neural networks NP hard?
In fact, there does not exist an algorithm to solve the problem of finding an optimal set of weights for a neural network in polynomial time. Mathematically, the optimization problem solved by training a neural network is referred to as NP-complete (e.g. they are very hard to solve).
Is neural network an NP?
Theorem: Training a 3-node neural network is NP-complete. one hidden node are required to equal the corresponding weights of the other, so possibly only the thresholds differ, and even if any or all of the weights are forced to be from {+ 1, -I}. 2.
Is Machine Learning NP-hard?
So, we don’t think there’s much hope for machine learning researchers to try to find algorithms that are provably optimal for deep networks. This is because the problem is NP-hard, meaning that provably solving it in polynomial time would also solve thousands of open problems that have been open for decades.
Can machine learning solve NP problems?
Q: Will machine learning prove useful in solving NP Complete problems? A: The short answer is “If you’re an engineer, yes. If you’re a mathematician, no.” but there’s more to it. “Solving” a problem means something very different to a mathematician than to an engineer.
Is the optimization problem solved by training a neural network?
Mathematically, the optimization problem solved by training a neural network is referred to as NP-complete (e.g. they are very hard to solve). We prove this problem NP-complete and thus demonstrate that learning in neural networks has no efficient general solution. — Neural Network Design and the Complexity of Learning, 1988.
Why is it hard to train a neural network?
The iterative training process of neural networks solves an optimization problem that finds for parameters (model weights) that result in a minimum error or loss when evaluating the examples in the training dataset.
Can a 3 node neural network be NP complete?
We consider a 2-layer, 3-node, n-input neural network whose nodes compute linear threshold functions of their inputs. We show that it is NP-complete to decide whether there exist weights and thresholds for the three nodes of this network so that it will produce output con sistent with a given set of training examples.
How is the fitting of a neural network done?
Fitting a neural network involves using a training dataset to update the model weights to create a good mapping of inputs to outputs.