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.

sleep paralysis is a parasomnia, events associated with sleep.

sleep paralysis happens when you are in the middle of waking up or falling asleep. it is the feeling of being conscious but unable to move. it definitely involves hallucinations.

i had a friend that had sleep paralysis and he told me a story one time where he was laying in bed and he couldn't move his body no matter how hard he tried and there was someone standing in the corner of his room coming towards him. he couldn't move and you can't "wake up" because you are already conscious. sometimes sleep paralysis can be triggered by frequent waking up during the night based off of multiple trips to the bathroom, or if you had ever been abused, sleep paralysis can start happening from things like that.

a is the best answer your welcome

if the person is shown to have +90n, then ans is -10n.

if the person is shown to have -90n, then ans is +10n.