Physics, 17.07.2019 17:20 kaitlyn0123
gradient descent: consider the code example discussed in class. the prediction function is 1 +ws and the loss function is y(w, i) t where t represents the vector of observed targets, and x represents the vector of observed features. (a) derive the gradient and hessian of l(wx), with respect to w b) implement them and re-run the example, playing around with the step size and starting values do you see how much work it took to get get newton's method to converge to something sensible? (c) modify the code to give you stochastic gradient descent. try this with different mini-batch sizes and starting values, to get a feel for how it works- particularly the stability of the algorithm with respect to these hyper-parameters.
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Physics, 22.06.2019 04:00
Simple machines can a. increase force only b. decrease force only c. increase or decrease force
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Physics, 22.06.2019 04:30
Asystem containing an ideal gas at a constant pressure of 1.22×10^5 pa gains 2140 j of heat. during the process, the internal energy of the system increases by 2320 j. what is the change in volume of the gas?
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Physics, 22.06.2019 08:00
You have a pick-up truck that weighed 4,000 pounds when it was new. you are modifying it to increase its ground clearance. when you are finished
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gradient descent: consider the code example discussed in class. the prediction function is 1...
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