The XOR problem is famously solvable by adding a layer to a single layer perceptron assuming a unit step function. This is a very basic exercise taught in many

The XOR problem is famously solvable by adding a layer to a single layer perceptron assuming a unit step function. This is a very basic exercise taught in many intro courses. I agree with every second sentence: Given that every perceptron layer does a linear transformation of the input.<- True There is no linear function that separates XOR, and that also means there is no sequence of linear functions that separates XOR. <- False That means there is no 1-layer perceptron that can learn XOR, <- True and there is no multilayer perceptron that can learn XOR.<-False Please look it up. Here are a few links: http://toritris.weebly.com/perceptron-5-xor-how--why-neurons-work-together.html http://toritris.weebly.com/perceptron-5-xor-how--why-neurons... The graph in slide 3 of this link helps explain it: http://www.di.unito.it/~cancelli/retineu11_12/FNN.pdf http://www.di.unito.it/~cancelli/retineu11_12/FNN.pdf http://www.mind.ilstu.edu/curriculum/artificial_neural_net/xor_problem_and_solution.php http://www.mind.ilstu.edu/curriculum/artificial_neural_net/x...