Gradient Descent Convergence2019 Community Moderator ElectionStochastic gradient descent based on vector operations?procedure for gradient descentRegression problem - too complex for gradient descentIntuition in Backpropagation (gradient descent)Algorithm to apply Lasso and Ridge in Gradient descentGradient descent and partial derivativesStochastic Gradient Descent BatchingL-BFGS improvement for stochastic gradient descentLinear classifier and gradient descentHow do I approach learning Data Science/ML the 'rightest' way?
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Gradient Descent Convergence
2019 Community Moderator ElectionStochastic gradient descent based on vector operations?procedure for gradient descentRegression problem - too complex for gradient descentIntuition in Backpropagation (gradient descent)Algorithm to apply Lasso and Ridge in Gradient descentGradient descent and partial derivativesStochastic Gradient Descent BatchingL-BFGS improvement for stochastic gradient descentLinear classifier and gradient descentHow do I approach learning Data Science/ML the 'rightest' way?
$begingroup$
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 32 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 32 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 32 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 32 mins ago
bladeblade
1033
asked 32 mins ago
bladeblade
1033
asked 32 mins ago
bladeblade
1033
asked 32 mins ago
bladeblade
1033
asked 32 mins ago
bladeblade
1033
1033
add a comment |
add a comment |
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