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Mlbasic

深度学习常用优化方法详解

From tsycnh·Updated November 14, 2025·View on GitHub·

* p1: 最基本的梯度下降法：gradient descent * p2: 原始 SGD方法：stochastic gradient descent * p3: minibatch-SGD方法 * p4 momentum SGD: minibatch-SGD with momentum * p4 momentum: momentum with SGD * p5: Nesterov方法 * p6: adagrad * p7: adadelta * p8: adam The project is written primarily in Python, first published in 2017. Key topics include: gradient-descent, minibatch-sgd, momentum, sgd.

一些关于机器学习优化函数的练习

  • p1: 最基本的梯度下降法：gradient descent
  • p2: 原始 SGD方法：stochastic gradient descent
  • p3: minibatch-SGD方法
  • p4 momentum SGD: minibatch-SGD with momentum
  • p4 momentum: momentum with SGD
  • p5: Nesterov方法
  • p6: adagrad
  • p7: adadelta
  • p8: adam

参考

p1 参考 https://zhuanlan.zhihu.com/p/27297638

p2~pn 参考 http://ruder.io/optimizing-gradient-descent/index.html

单独参考

p5 参考 http://cs231n.github.io/neural-networks-3/

p6 参考 https://zhuanlan.zhihu.com/p/22252270

p7 参考 https://arxiv.org/abs/1212.5701 (原始论文)

p8 参考 http://www.ijiandao.com/2b/baijia/63540.html

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tsycnh

tsycnh

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Stanley-Tian

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This article is auto-generated from tsycnh/mlbasic via the GitHub API.Last fetched: 6/14/2026