Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...
50 KB (6,600 words) - 10:13, 31 May 2024
of gradient descent, stochastic gradient descent, serves as the most basic algorithm used for training most deep networks today. Gradient descent is based...
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Federated learning (redirect from Federated stochastic gradient descent)
of stochastic gradient descent, where gradients are computed on a random subset of the total dataset and then used to make one step of the gradient descent...
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Online machine learning (redirect from Incremental stochastic gradient descent)
out-of-core versions of machine learning algorithms, for example, stochastic gradient descent. When combined with backpropagation, this is currently the de...
25 KB (4,740 words) - 03:53, 2 May 2024
Backpropagation (section Second-order gradient descent)
can be derived through dynamic programming. Gradient descent, or variants such as stochastic gradient descent, are commonly used. Strictly the term backpropagation...
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Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a...
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Gradient descent Stochastic gradient descent Wolfe conditions Absil, P. A.; Mahony, R.; Andrews, B. (2005). "Convergence of the iterates of Descent methods...
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descent Stochastic gradient descent Coordinate descent FrankâWolfe algorithm Landweber iteration Random coordinate descent Conjugate gradient method Derivation...
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for all nodes in the tree. Typically, stochastic gradient descent (SGD) is used to train the network. The gradient is computed using backpropagation through...
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being stuck at local minima. One can also apply a widespread stochastic gradient descent method with iterative projection to solve this problem. The idea...
23 KB (3,496 words) - 08:22, 5 March 2024