Deep Learning Optimization in the Context of Deep Learning The Importance of Optimization in Deep Learning Why Should We Care? Why the Right Kind of Optimization May Be Helpful? Course Goal ML Basics Errors in Machine Learning Models Analyzing Estimation Error in Deep Learning Models 另一个error:...
分析SGD的理论 Constant v.s. diminishing learning rate 这是一个常量学习速率和衰减学习速率的争论问题,常量学习速率可能在最后收敛阶段收敛不到最小值,而是在震荡。但衰减学习可能会导致收敛速度很慢。 New analysis for constant learning rate: realizable case 针对上面的问题,也就是常量学习速率能不能收敛到最小...
比如会以epoch为单位(即完整遍历数据集大小次),仍然有放回的取数据(不放回当然也有,类online learning,以后讨论);这意味着每个epoch中便利数据N次(按随机顺序);不同epoch的遍历顺序由于随机性当然不同。 epoch-based reshuffling的算法。差别在于对epoch的区分,以及对zi的选取 mini-batch:一次选B个, SGD收敛性的...
Optimization is a critical component in deep learning.We think optimization for neural networks is an interesting topic for theoretical research due to various reasons.First,its tractability despite non-convexity is an intriguing question and may greatly expand our understanding of tractable problems....
除了上述方法,还有一些其他的设计来实现更好的神经网络, 数据预处理方法比如数据增强,adversrial training 优化方法如(optimization algo,learning rate schedule , learning rate dacay) 正则化方法(l2-norm,droo out) 神经网络架构,更深,更广,连接模式, 激活函数(RELU,Leak ReLU, tanh,swish等。
深度学习(Deep Learning)中最大的特点,就是大量使用深度网络的无监督学习(unsupervised learning)。但是监督学习仍然扮演着非常重要的角色。非监督预学习(pre-training)的作用在于,评估(在监督精细迭代(fine-tuning)之后)网络可以达到的性能。这节回顾了分类模型中监督学习的理论基础,并且包含了多数模型中精细迭代所需要的...
Learning rate warmup 在开始的时候使用非常小的learning rate,然后过几次迭代使用正常的learning rate。 这种方法在ResNet , Large-batch training, Transformer 或 BERT 中使用比较常见。 Cyclical learning rate. 在一个epoch中 让学习率在一个范围内上下波动 ...
Learn techniques for optimal model compression and optimization that reduce model size and enable them to run faster and more efficiently than before.
原文地址为:Deep learning:三十七(Deep learning中的优化方法) 内容: 本文主要是参考论文:On optimization methods fordeep learning,文章内容主要是笔记SGD(随机梯度下降),LBFGS(受限的BFGS),CG(共轭梯度法)三种常见优化算法的在deep learning体系中的性能。下面是一些读完的笔记。
For the moment, Adam is the most famous optimization algorithm in deep learning. At a high level, Adam combines Momentum and RMSProp algorithms. To achieve it, it simply keeps track of the exponentially moving averages for computed gradients and squared gradients respectively. ...