【Optimization Algorithms】的更多相关文章

(转) An overview of gradient descent optimization algorithms

An overview of gradient descent optimization algorithms Table of contents: Gradient descent variantsChallenges Batch gradient descent Stochastic gradient descent Mini-batch gradient descent Gradient descent optimization algorithms Momentum Nesterov a…

An overview of gradient descent optimization algorithms

原文地址:An overview of gradient descent optimization algorithms An overview of gradient descent optimization algorithms Note: If you are looking for a review paper, this blog post is also available as an article on arXiv. Update 15.06.2017: Added deriva…

课程二(Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization)，第二周（Optimization algorithms） —— 2.Programming assignments:Optimization

Optimization Welcome to the optimization's programming assignment of the hyper-parameters tuning specialization. There are many different optimization algorithms you could be using to get you to the minimal cost. Similarly, there are many different p…

优化算法动画演示Alec Radford's animations for optimization algorithms

Alec Radford has created some great animations comparing optimization algorithms SGD, Momentum, NAG, Adagrad, Adadelta,RMSprop (unfortunately no Adam) on low dimensional problems. Also check out his presentation on RNNs. "Noisy moons: This is logisti…

[C2W2] Improving Deep Neural Networks : Optimization algorithms

第二周:优化算法(Optimization algorithms) Mini-batch 梯度下降(Mini-batch gradient descent) 本周将学习优化算法,这能让你的神经网络运行得更快.机器学习的应用是一个高度依赖经验的过程,伴随着大量迭代的过程,你需要训练诸多模型,才能找到合适的那一个,所以,优化算法能够帮助你快速训练模型. 我们希望可以利用一个巨大的数据集来训练神经网络,而深度学习没有在大数据领域发挥最大的效果其中一个难点在于,在巨大的数据集基础上进行训练速度很慢.因此…

【论文翻译】An overiview of gradient descent optimization algorithms

这篇论文最早是一篇2016年1月16日发表在Sebastian Ruder的博客.本文主要工作是对这篇论文与李宏毅课程相关的核心部分进行翻译. 论文全文翻译: An overview of gradient descent optimization algorithms 梯度下降优化算法概述 0. Abstract 摘要: Gradient descent optimization algorithms, while increasingly popular, are often used as…

Optimization Algorithms

1. Stochastic Gradient Descent 2. SGD With Momentum Stochastic gradient descent with momentum remembers the update Δ w at each iteration, and determines the next update as a linear combination of the gradient and the previous update: Unlike in classi…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week2, Optimization algorithms

Gradient descent Batch Gradient Decent, Mini-batch gradient descent, Stochastic gradient descent 还有很多比gradient decent 更优化的算法,在了解这些算法前,需要先理解  Exponentially weighted averages 这个概念 Exponentially weighted average 是一种计算平均值的方法,非常省storage 和 memory, 但是不是很精确.…

An overview of gradient descent optimization algorithms (更新到Adam)

Momentum:解快了收敛速度,同时也减弱了SGD的波动 NAG: 减速了Momentum更新参数太快 Adagrad: 出现频率较低参数采用较大的更新,对于出现频率较高的参数采用较小的,不共用一个学习率 Adadelta:解决了Adagrad后续学习率为0的缺点,同时不要defalut 学习率 RMSprop:解决了Adagrad后续学习率为0的缺点 Adam: 结合了RMSprop和Momentum的优点,Adam might be the best overall choice 参考博客…

最佳化常用测试函数 Optimization Test functions

http://www.sfu.ca/~ssurjano/optimization.html The functions listed below are some of the common functions and datasets used for testing optimization algorithms. They are grouped according to similarities in their significant physical properties and s…