Basic logistic regression question
From lecture 9 page 23 of the slides there is an algorithm of how to implement logistic regression. In step 3 it explains how to compute the gradient. Is the Ein value actually a vector or is it a single number? If it's a single number then the weight would be the same for every value in the weight vector so it seems like Ein is a vector. Is my understanding correct?
And if it's a vector then I'm a little unclear on how to compute the values. Each training point has two values x1 and x2 and an outcome y. So to calculate Ein do I just use x1 and weight1 to find the first value and then use x2 and weight2 to find the second value? Hopefully the above makes sense, I seem to be struggling with something that seems like it should be pretty simple. 
Re: Basic logistic regression question
Okay I think I see how that works but I'm still struggling to understand Q8. In the question I set the weights to 0. Then the first time through the loop I will calculate the gradient of E_in using the formula in step 3. Because w is all zeros the denominator will end up as 1+ e^0 == 2. The numerator can at most be +/1/ So the biggest change in gradient is +/0.5 for each weight.
Then in step 4 I update the weights w(1) = w0  learningRate*gradient. w(1) = 0,0,0  0.01(0.5,0.5,0.5) w(1) = (0.005,0.005,0.005) Now the question states stop the algorithm when w(t1) and w(t) < 0.01. So: sqrt((00.005)^2+(00.005)^2+(00.005)^2) = 0.008 So based on the values above the algorithm will stop after the first iteration because the difference in weights is < 0.01. Have I misunderstood the gradient of E_in formula? Or am I calculating my error incorrectly? I've tried using batch gradient descent and see the above results (I have 100 data points but the error still ends up less than 0.01.) I've also tried stochastic gradient descent and have similar problems. I've watched lecture 9 a couple of times now and seem to understand how the algorithm works but I guess my understanding isn't complete. Any suggestions would be most appreciated. 
Re: Basic logistic regression question
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