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…

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…

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…

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…

梯度下降法作为一种反向传播算法最早在上世纪由geoffrey hinton等人提出并被广泛接受.最早GD由很多研究团队各自发表,可他们大多无人问津,而hinton做的研究完整表述了GD方法,同时hinton为自己的研究多次走动人际关系使得其论文出现在了当时的<nature>上,从此GD开始得到业界的关注.这为后面各种改进版GD的出现与21世纪深度学习的大爆发奠定了最重要的基础. PART1:original版的梯度下降法 首先已经有了 对weights和bias初始化过的神经网络计算图,也有一…

Introduction Optimization is always the ultimate goal whether you are dealing with a real life problem or building a software product. I, as a computer science student, always fiddled with optimizing my code to the extent that I could brag about its…

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…

solver : {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’}, default: ‘liblinear’ Algorithm to use in the optimization problem. For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ is faster for large ones. For multiclass problems, only ‘newton-cg…

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…

FITTING A MODEL VIA CLOSED-FORM EQUATIONS VS. GRADIENT DESCENT VS STOCHASTIC GRADIENT DESCENT VS MINI-BATCH LEARNING. WHAT IS THE DIFFERENCE? In order to explain the differences between alternative approaches to estimating the parameters of a model,…