Question

Draw a neural network that represents the function f(x1, x2, x3) defined below. You can only use two types of units: linear units and sign units. Recall that the linear unit takes as input weights and attribute values and outputs w0 + P i wixi , while the sign unit outputs +1 if (w0 + P i wixi) > 0 and −1 otherwise

Answer 1

To represent the function f(x1, x2, x3) using a neural network, we can use linear units for the inputs and a sign unit for the output.

Step-by-step explanation:

To represent the function f(x1, x2, x3) using a neural network, we can use linear units for the inputs and a sign unit for the output. Let's assume we have three inputs, x1, x2, and x3. We can represent them as the inputs of the linear units. Now, let's assume we have weights w0, w1, w2, and w3 for the linear units. We can calculate the output of each linear unit using the formula w0 + w1 * x1 + w2 * x2 + w3 * x3. Finally, we can use a sign unit to determine the final output of the neural network. If the calculated output is greater than 0, the sign unit will output +1; otherwise, it will output -1.