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…

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…

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…

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…

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…

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…

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,…

Logistic Regression and Gradient Descent Logistic regression is an excellent tool to know for classification problems. Classification problems are problems where you are trying to classify observations into groups. To make our examples more concrete,…

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…