本文以下内容来自读论文以后认为有价值的地方,论文来自:convolutional Neural Networks Applied to House Numbers Digit Classification . 对于房门号的数字识别问题,文中提出的方法是基于卷积神经网络的,卷积神经网络集特征提取与目标分类于一体,这一点有别于传统的识别方法(传统方法中一般都是基于人工设计的特征提取器,然后把提取到的特征输入给分类器). 文中在传统的卷积神经网络基础上有两点改进: 第一:pooling层,传统的方法的…

CNN综述文章 的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks 翻译 综述深度卷积神经网络架构:从基本组件到结构创新 目录 摘要    1.引言    2.CNN基本组件        2.1 卷积层        2.2 池化层        2.3 激活函数        2.4 批次归一化        2.5 Dropout        2.6 全连接层…

http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ 讲CNN以及其在NLP的应用,非常深入浅出的讲法,好文,mark. When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakt…

When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated pho…

ImageNet Classification with Deep Convolutional Neural Networks 深度卷积神经网络的ImageNet分类 Alex Krizhevsky University of Toronto 多伦多大学 kriz@cs.utoronto.ca Ilya Sutskever University of Toronto 多伦多大学 ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toront…

ImageNet Classification with Deep Convolutional Neural Networks 摘要 我们训练了一个大型深度卷积神经网络来将ImageNet LSVRC-2010竞赛的120万高分辨率的图像分到1000不同的类别中.在测试数据上,我们得到了top-1 37.5%, top-5 17.0%的错误率,这个结果比目前的最好结果好很多.这个神经网络有6000万参数和650000个神经元,包含5个卷积层(某些卷积层后面带有池化层)和3个全连接层,最后是一个1…

论文标题:MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 论文作者:Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam 论文地址:https://arxiv.org/abs/1704.04861…

This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, discuss our model and its possible applications to Flipboa…

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns C…

Image Scaling using Deep Convolutional Neural Networks This past summer I interned at Flipboard in Palo Alto, California. I worked on machine learning based problems, one of which was Image Upscaling. This post will show some preliminary results, dis…