Why don't we use non-constant learning rates for gradient decent for things other then neural networks? – stats.stackexchange.com 11:14 Posted by Unknown No Comments Deep learning literature is full of clever tricks with using non-constant learning rates in gradient descent. Things like exponential decay, RMSprop, Adagrad etc. are easy to implement and are ... from Hot Questions - Stack Exchange OnStackOverflow via Blogspot Share this Google Facebook Twitter More Digg Linkedin Stumbleupon Delicious Tumblr BufferApp Pocket Evernote Unknown Artikel TerkaitTime complexity to find all pairs in an array – stackoverflow.comTuring machine + time dilation = solve the halting problem? – cs.stackexchange.comIf a monk reduces damage to 0 using Deflect Missiles, does the attack still hit? – rpg.stackexchange.comSince voltage determines an electron's energy, why is it current rather than voltage that is harmful? – physics.stackexchange.comIs the sum of two sine waves periodic? What will be the period of sin(mx)+sin(nx)? – math.stackexchange.comA very simple one – puzzling.stackexchange.com
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