# 梯度下降算法

## 梯度下降算法

* [返回顶层目录](https://luweikxy.gitbook.io/machine-learning-notes/summary#目录)
* [各类梯度下降算法的演化](#各类梯度下降算法的演化)
* [随机梯度下降SGD](https://luweikxy.gitbook.io/machine-learning-notes/broken-reference)
* [动量法Momentum](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/momentum)
* [牛顿动量Nesterov](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/nesterov)
* [AdaGrad](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/adagrad)
* [RMSprop](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/rmsprop)
* [Adadelta](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/adadelta)
* [Adam](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/adam)
* [Nadam](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/nadam)
* [AMSGrad](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/amsgrad)
* [AdasMax](https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm/adamax)

![optimization-on-loss-surface-contours](https://3298324061-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LpO5sn2FY1C9esHFJmo%2F-LurJHbNKPiaarTTRnqJ%2F-LurJLn_2Peov9ITKLo3%2Foptimization-on-loss-surface-contours.gif?generation=1575033598545052\&alt=media)

## 各类梯度下降算法的演化

![revolution-of-gradient-descent](https://3298324061-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LpO5sn2FY1C9esHFJmo%2F-Lug-9Dm3wMqPuGpCQ0c%2F-Lug-Cj8eWQsYrsBcgKw%2Frevolution-of-gradient-descent.jpeg?generation=1574843768973331\&alt=media)

## 参考资料

* [Deep Learning 之 最优化方法](https://blog.csdn.net/BVL10101111/article/details/72614711)
* [从 SGD 到 Adam —— 深度学习优化算法概览(一)](https://zhuanlan.zhihu.com/p/32626442)
* [深度学习最全优化方法总结比较（SGD，Adagrad，Adadelta，Adam，Adamax，Nadam）](https://zhuanlan.zhihu.com/p/22252270)
* [10个梯度下降优化算法+备忘单](https://ai.yanxishe.com/page/TextTranslation/1603?from=singlemessage)

  [深度学习——优化器算法Optimizer详解（BGD、SGD、MBGD、Momentum、NAG、Adagrad、Adadelta、RMSprop、Adam）](https://www.cnblogs.com/guoyaohua/p/8542554.html)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://luweikxy.gitbook.io/machine-learning-notes/gradient-descent-algorithm.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.