Gradient Descent Convergence
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I'm a double major in Math and CS interested in Machine Learning. I'm currently taking the popular Coursera course by Prof. Andrew. He's talking and explaining Gradient Descent but I can't avoid noticing a few things. With my math background, I know that if I'm trying to find the global min/max of a function, I must first find all the critical points first. The course talks about convergence of GD, but is it really guaranteed to converge to the global min? How do I know it won't get stuck at a saddle point? Wouldn't be safer to do a 2nd derivative test to test it? If my function is differentiable it seems reasonable it converges to a local min, but not to the global min. I have tried looking for a better explanation but everyone seems to take it for granted without questioning. Can someone point me in the right direction?
machine-learning regression gradient-descent
asked 2 mins ago
bladeblade
1033
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add a comment |
$begingroup$
I'm a double major in Math and CS interested in Machine Learning. I'm currently taking the popular Coursera course by Prof. Andrew. He's talking and explaining Gradient Descent but I can't avoid noticing a few things. With my math background, I know that if I'm trying to find the global min/max of a function, I must first find all the critical points first. The course talks about convergence of GD, but is it really guaranteed to converge to the global min? How do I know it won't get stuck at a saddle point? Wouldn't be safer to do a 2nd derivative test to test it? If my function is differentiable it seems reasonable it converges to a local min, but not to the global min. I have tried looking for a better explanation but everyone seems to take it for granted without questioning. Can someone point me in the right direction?
machine-learning regression gradient-descent
asked 2 mins ago
bladeblade
1033
$endgroup$
add a comment |
$begingroup$
I'm a double major in Math and CS interested in Machine Learning. I'm currently taking the popular Coursera course by Prof. Andrew. He's talking and explaining Gradient Descent but I can't avoid noticing a few things. With my math background, I know that if I'm trying to find the global min/max of a function, I must first find all the critical points first. The course talks about convergence of GD, but is it really guaranteed to converge to the global min? How do I know it won't get stuck at a saddle point? Wouldn't be safer to do a 2nd derivative test to test it? If my function is differentiable it seems reasonable it converges to a local min, but not to the global min. I have tried looking for a better explanation but everyone seems to take it for granted without questioning. Can someone point me in the right direction?
machine-learning regression gradient-descent
asked 2 mins ago
bladeblade
1033
$endgroup$
I'm a double major in Math and CS interested in Machine Learning. I'm currently taking the popular Coursera course by Prof. Andrew. He's talking and explaining Gradient Descent but I can't avoid noticing a few things. With my math background, I know that if I'm trying to find the global min/max of a function, I must first find all the critical points first. The course talks about convergence of GD, but is it really guaranteed to converge to the global min? How do I know it won't get stuck at a saddle point? Wouldn't be safer to do a 2nd derivative test to test it? If my function is differentiable it seems reasonable it converges to a local min, but not to the global min. I have tried looking for a better explanation but everyone seems to take it for granted without questioning. Can someone point me in the right direction?
machine-learning regression gradient-descent
machine-learning regression gradient-descent
asked 2 mins ago
bladeblade
1033
asked 2 mins ago
bladeblade
1033
asked 2 mins ago
bladeblade
1033
asked 2 mins ago
bladeblade
1033
asked 2 mins ago
bladeblade
1033
1033
add a comment |
add a comment |
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