原文:written by Sebastian Raschka on March 14, 2015 中文版译文:伯乐在线 - atmanic 翻译,toolate 校稿 This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network…

今天看到一篇关于检测的论文<SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving>,论文中的效果还不错,后来查了一下,有一个Tensorflow版本的实现,因此在自己的机器上配置了Tensorflow的环境,然后将其给出的demo跑通了,其中遇到了一些小问题,通过查找网络上的资料解决掉了,在这里…

理论知识:Deep learning:四十一(Dropout简单理解).深度学习(二十二)Dropout浅层理解与实现.“Improving neural networks by preventing co-adaptation of feature detectors” 感觉没什么好说的了,该说的在引用的这两篇博客里已经说得很清楚了,直接做试验吧 注意: 1.在模型的测试阶段,使用”mean network(均值网络)”来得到隐含层的输出,其实就是在网络前向传播到输出层前时隐含层节点的输出值都…

别看本文没有几页纸,本着把经典的文多读几遍的想法,把它彩印出来看,没想到效果很好,比在屏幕上看着舒服.若用蓝色的笔圈出重点,这篇文章中几乎要全蓝.字字珠玑. Reducing the Dimensionality of Data with Neural Networks G.E. Hinton and R.R. Salakhutdinov  摘要 训练一个带有很小的中间层的多层神经网络,可以重构高维空间的输入向量,实现从高维数据到低维编码的效果.(原文为high-dimensional data…

5 Neural Networks (part two) content: 5 Neural Networks (part two) 5.1 cost function 5.2 Back Propagation 5.3 神经网络总结 接上一篇4. Neural Networks (part one).本文将先定义神经网络的代价函数,然后介绍逆向传播(Back Propagation: BP)算法,它能有效求解代价函数对连接权重的偏导,最后对训练神经网络的过程进行总结. 5.1 cost func…

4. Neural Networks (part one) Content: 4. Neural Networks (part one) 4.1 Non-linear Classification. 4.2 Neural Model(神经元模型) 4.3 Forward Propagation 4.4 神经网络实现与或非门以及异或门 4.4.1 实现与或非门(AND/OR/NOT) 4.4.2 实现异或/同或门(XOR/XNOR) 4.5 Multi-class classification k…

这是Jake Bouvrie在2006年写的关于CNN的训练原理,虽然文献老了点,不过对理解经典CNN的训练过程还是很有帮助的.该作者是剑桥的研究认知科学的.翻译如有不对之处,还望告知,我好及时改正,谢谢指正! Notes on Convolutional Neural Networks Jake Bouvrie 2006年11月22 1引言 这个文档是为了讨论CNN的推导和执行步骤的,并加上一些简单的扩展.因为CNN包含着比权重还多的连接,所以结构本身就相当于实现了一种形式的正则化了.另外CN…

<ImageNet Classification with Deep Convolutional Neural Networks> 剖析 CNN 领域的经典之作, 作者训练了一个面向数量为 1.2 百万的高分辨率的图像数据集ImageNet, 图像的种类为1000 种的深度卷积神经网络.并在图像识别的benchmark数据集上取得了卓越的成绩. 和之间的LeNet还是有着异曲同工之妙.这里涉及到 category 种类多的因素,该网络考虑了多通道卷积操作, 卷积操作也不是 LeNet 的单通道…

零.说明: 本文的所有代码均可在 DML 找到,欢迎点星星. 注.CNN的这份代码非常慢,基本上没有实际使用的可能,所以我只是发出来,代表我还是实践过而已 一.引入: CNN这个模型实在是有些年份了,最近随着深度学习的兴起又开始焕发青春了,把imagenet测试的准确度提高了非常多,一个是Alex的工作,然后最近好像Zeiler又有突破性的成果,可惜这些我都没看过,主要是imagenet的数据太大了,我根本没有可能跑得动,所以学习的积极性有些打折扣.不说那么多,还是先实现一个最基础的CNN再说吧…

前言 论文“Reducing the Dimensionality of Data with Neural Networks”是深度学习鼻祖hinton于2006年发表于<SCIENCE >的论文,也是这篇论文揭开了深度学习的序幕. 笔记 摘要:高维数据可以通过一个多层神经网络把它编码成一个低维数据,从而重建这个高维数据,其中这个神经网络的中间层神经元数是较少的,可把这个神经网络叫做自动编码网络或自编码器(autoencoder).梯度下降法可用来微调这个自动编码器的权值,但是只有在初始化权值…

colah's blog Blog About Contact Neural Networks, Manifolds, and Topology Posted on April 6, 2014 topology, neural networks, deep learning, manifold hypothesis Recently, there’s been a great deal of excitement and interest in deep neural networks beca…

循环神经网络(RNN, Recurrent Neural Networks)介绍    这篇文章很多内容是参考:http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/,在这篇文章中,加入了一些新的内容与一些自己的理解.   循环神经网络(Recurrent Neural Networks,RNNs)已经在众多自然语言处理(Natural Language Proce…

A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential…

The Impact of Imbalanced Training Data for Convolutional Neural Networks Paulina Hensman and David Masko 摘要 本论文从实验的角度调研了训练数据的不均衡性对采用CNN解决图像分类问题的性能影响.CIFAR-10数据集包含10个不同类别的60000个图像,用来构建不同类间分布的数据集.例如,一些训练集中包含一个类别的图像数目与其他类别的图像数目比例失衡.用这些训练集分别来训练一个CNN,度量其得…

http://handong1587.github.io/deep_learning/2015/10/09/training-dnn.html  //转载于 Training Deep Neural Networks  Published: 09 Oct 2015  Category: deep_learning Tutorials Popular Training Approaches of DNNs — A Quick Overview https://medium.com/@asjad/p…

课程主页:http://cs231n.stanford.edu/   Introduction to neural networks -Training Neural Network ______________________________________________________________________________________________________________________________________________________________…

Hacker's guide to Neural Networks Hi there, I'm a CS PhD student at Stanford. I've worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Net…

ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS We recently interviewed Reza Zadeh (@Reza_Zadeh). Reza is a Consulting Professor in the Institute for Computational and Mathematical Engineering at Stanford University and a…

原文 http://blog.csdn.net/abcjennifer/article/details/7758797 本栏目(Machine learning)包括单参数的线性回归.多参数的线性回归.Octave Tutorial.Logistic Regression.Regularization.神经网络.机器学习系统设计.SVM(Support Vector Machines 支持向量机).聚类.降维.异常检测.大规模机器学习等章节.所有内容均来自Standford公开课machine…

原文 http://blog.csdn.net/abcjennifer/article/details/7749309 本栏目(Machine learning)包括单参数的线性回归.多参数的线性回归.Octave Tutorial.Logistic Regression.Regularization.神经网络.机器学习系统设计.SVM(Support Vector Machines 支持向量机).聚类.降维.异常检测.大规模机器学习等章节.所有内容均来自Standford公开课machine…

一. 预备知识 包括 Linear Regression, Logistic Regression和 Multi-Layer Neural Network.参考 http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/ 或者coursera看Andrew Ng 的机器学习课程.二者只是在某些公式表达上有细微的差距. 二. 卷积神经网络CONVNET 此部分来自 http://m.blog.csdn.net/ar…

When a golf player is first learning to play golf, they usually spend most of their time developing a basic swing. Only gradually do they develop other shots, learning to chip, draw and fade the ball, building on and modifying their basic swing. In a…

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…

The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and this week's videos explain the 'backprogagation' algorithm for training these models. In this week's progr…

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets(解释口译) and understand your voice commands, it is likely that a neural network is helping to understand your speech; wh…

Attention and Augmented Recurrent Neural Networks CHRIS OLAHGoogle Brain SHAN CARTERGoogle Brain Sept. 8 2016 Citation: Olah & Carter, 2016 Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with seque…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do reali…

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking  arXiv Paper Project Page:http://guanghan.info/projects/ROLO/ GitHub:https://github.com/wangxiao5791509/ROLO 摘要:本文提出了一种新的方法进行空间监督 RCNN 来进行目标跟踪.我们通过深度神经网络来学习到  loc…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but…

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 本文提出了一种新的CNN 框架来处理跟踪问题.众所周知,CNN在很多视觉领域都是如鱼得水,唯独目标跟踪显得有点“慢热”,这主要是因为CNN的训练需要海量数据,纵然是在ImageNet 数据集上微调后的model 仍然不足以很好的表达要跟踪地物体,因为Tracking问题的特殊性,至于怎么特殊的,且听细细道来. 目标跟踪之所以很少被 C…