1. The problem statement, all variables and given/known data I am trying to train a neural network using the following training set: (4,6) (9,10) (1,2) (6,2) So, if I enter input 4, it should give me 6. If I enter 9, output should be 10. If I enter 3.5, it should approximate an output based on the training examples. 2. Relevant equations weight update rule: w_new = w_old + (-learning rate) * (error) * (input) 3. The attempt at a solution So, lets say I randomly initialize the weights and get w1 = 3 and w0 = 4 For the first example, (4,6) The input will be (4,1) because of the intercept. 4*3 + 4*1 = 16 The difference between the target value and the actual output is 10. Now, I just need help with the weight update rule because I am not sure how to proceed. For example: Can I subtract 4 from the error because of the intercept to get error = 6 learning rate = -.1 w_new = 3 + (-.1)*(6)*(4) w_new = .6 Then to test this I get: 4*.6 + 4*1 = 6.4 Any help greatly appreciated!