The correct answer is: B. false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
A perceptron is a type of artificial neuron that can be used to classify data. It has a single input, a single output, and a weight associated with each input. The output of the perceptron is calculated by multiplying the inputs by their weights and then adding them together. If the sum is greater than a certain threshold, the output is 1; otherwise, the output is 0.
A linearly inseparable function is a function that cannot be represented by a linear combination of perceptrons. The XOR function is an example of a linearly inseparable function.
No matter how many perceptrons you use, you cannot solve the XOR problem with a single layer of perceptrons. This is because the XOR function is not linearly separable.
There are a few ways to solve the XOR problem with neural networks. One way is to use a two-layer neural network. In a two-layer neural network, the first layer of perceptrons is followed by a second layer of perceptrons. The outputs of the first layer are connected to the inputs of the second layer. The weights of the connections between the layers are learned during training.
Another way to solve the XOR problem with neural networks is to use a convolutional neural network (CNN). A CNN is a type of neural network that is specifically designed for image recognition. CNNs have been shown to be very effective at solving a variety of problems, including the XOR problem.
In conclusion, perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do. The XOR function is an example of a linearly inseparable function. There are a few ways to solve the XOR problem with neural networks, such as using a two-layer neural network or a convolutional neural network.