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Can neural network solve any problem?
A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly. If you accept most classes of problems can be reduced to functions, this statement implies a neural network can, in theory, solve any problem.
Is the world a neural network?
It’s a lot to process. One scientist says the universe is a giant neural net. The wild concept uses neural net theory to unify quantum and classical mechanics.
Can a single layer neural network solve the XOR problem?
In a word, I can understand why the single layer neural network can’t solve the XOR problem but I can’t understand how the two layers neural network work to solve it. Notice that the first table (orange line) is performing an OR operation and the second table (blue line) is performing an AND operation.
How to solve XOR problem with MLP neural network?
Take an arbitrary theta, let’s say 0.5. For each line, you need one hidden node and then combine things together while taking the negation into account. How to solve XOR problem with MLP neural network? Try plotting the sample space of an XOR function of two variables x1 and x2.
Do you need two lines to solve the XOR problem?
In the link above, it is talking about how the neural work solves the XOR problem. It says that we need two lines to separate the four points. But I don’t know the second table. In my opinion, the first table is OK because it includes the XOR, which means that what the second table need to do is to remove the forth input.
Which is the correct activation function for XOR problem?
The activation function in output layer is selected based on the output space. For a binary classification task sigmoid activations is correct choice while for multi class classification softmax is the most populary choice. In our X-OR problem, output is either 0 or 1 for each input sample. So, it is a two class or binary classification problem.