What are some good resources for learning about Artificial Neural Networks

http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks…

论文笔记之：Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

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…

[CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural networks论文笔记

p.p1 { margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px "Helvetica Neue"; color: #323333 } p.p2 { margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px "Helvetica Neue"; color: #042eee } span.s1 { } span.s2 { text-decoration: underline } Is objec…

（转）Understanding, generalisation, and transfer learning in deep neural networks

Understanding, generalisation, and transfer learning in deep neural networks FEBRUARY 27, 2017 This is the first in a series of posts looking at the ‘top 100 awesome deep learning papers.’ Deviating from the normal one-paper-per-day format, I’ll ta…

This instability is a fundamental problem for gradient-based learning in deep neural networks. vanishing exploding gradient problem

The unstable gradient problem: The fundamental problem here isn't so much the vanishing gradient problem or the exploding gradient problem. It's that the gradient in early layers is the product of terms from all the later layers. When there are many…

【论文阅读】Learning Dual Convolutional Neural Networks for Low-Level Vision

论文阅读([CVPR2018]Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision) 本文针对低层视觉问题,提出了一般性的用于解决低层视觉问题的对偶卷积神经网络.作者认为,低层视觉问题,如常见的有超分辨率重建.保边滤波.图像去雾和图像去雨等,这些问题经常涉及到估计目标信号的两个成分:结构和细节.因此,文章提出DualCNN,它包含两个平行的分支来分别恢复结构和细节信息. 具体内容参见https…

[译]深度神经网络的多任务学习概览(An Overview of Multi-task Learning in Deep Neural Networks)

译自:http://sebastianruder.com/multi-task/ 1. 前言在机器学习中,我们通常关心优化某一特定指标,不管这个指标是一个标准值,还是企业KPI.为了达到这个目标,我们训练单一模型或多个模型集合来完成指定得任务.然后,我们通过精细调参,来改进模型直至性能不再提升.尽管这样做可以针对一个任务得到一个可接受得性能,但是我们可能忽略了一些信息,这些信息有助于在我们关心的指标上做得更好.具体来说,这些信息就是相关任务的监督数据.通过在相关任务间共享表示信息,我们的模型在…

DAG-GNN: DAG Structure Learning with Graph Neural Networks

目录概主要内容代码 Yu Y., Chen J., Gao T. and Yu M. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning (ICML), 2019. 概有向无环图 + GNN + VAE. 主要内容先前已经有工作(NOTEARS)讨论了如何处理线性SEM模型 \[X = A^TX + Z, \] \(A \i…

Deep Learning Tutorial - Convolutional Neural Networks(LENET)

CNN很多概述和要点在CS231n.Neural Networks and Deep Learning中有详细阐述,这里补充Deep Learning Tutorial中的内容.本节前提是前两节的内容,因为要用到全连接层.logistic regression层等.关于Theano:掌握共享变量,下采样,conv2d,dimshuffle的应用等. 1.卷积操作在Theano中,ConvOp是提供卷积操作的主力.ConvOp来自theano.tensor.signal.conv.conv2d,…

【DeepLearning学习笔记】Coursera课程《Neural Networks and Deep Learning》——Week2 Neural Networks Basics课堂笔记

Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week2 Neural Networks Basics 2.1 Logistic Regression as a Neutral Network 2.1.1 Binary Classification 二分类逻辑回归是一个用于二分类(binary classification)的算法.首先我们从一个问题开始说起,这里有一个二分类问题的例子,假如你有一张图片作为输入,比…

Coursera, Deep Learning 4, Convolutional Neural Networks - week4,

Face recognition One Shot Learning 只看一次图片,就能以后识别, 传统deep learning 很难做到这个. 而且如果要加一个人到数据库里面,就要重新train model 显然不合理,所以就引出了 One Shot Learning 的概念. 怎么得出这个similarity function d(img1, img2) 呢?下面的介绍的 Siamese network.可以实现这个目标. 怎么定义object function 来满足上面的的条件呢?可以…

Coursera, Deep Learning 4, Convolutional Neural Networks - week2

Case Study (Note: 红色表示不重要) LeNet-5 起初用来识别手写数字灰度图片 AlexNet 输入的是227x227x3 的图片,输出1000 种类的结果 VGG VGG比AlexNet 结构更简单,filter 都是3x3的,max-pool 都是 2x2的. ResNets (Residual Network) 可用让很深的network 工作的很好. This really helps with the vanishing and exploding gradient…

Coursera, Deep Learning 4, Convolutional Neural Networks - week1

CNN 主要解决 computer vision 问题,同时解决input X 维度太大的问题. Edge detection 下面演示了convolution 的概念下图的 vertical edge 看起来有点厚,但是如果图片远比6x6像素大的话,就会看到效果非常不错. 除了前面讲过的第一种filter, 还有两种 (Sobel filter, Scharr filter) 接下来会讲到 CNN 的两个重要的buiding block - padding, strided convolut…

论文阅读：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

前言 CVPR2016 来自Korea的POSTECH这个团队大部分算法(例如HCF, DeepLMCF)只是用在大量数据上训练好的(pretrain)的一些网络如VGG作为特征提取器,这些做法证实利用CNN深度特征对跟踪结果有显著提升. 但是毕竟clssification 和 tracking是两个不同的课题 (predicting object class labels VS locating targets of arbitrary classes.) 所以作者设计了一个网络来做跟踪…

[Machine Learning]学习笔记-Neural Networks

引子对于一个特征数比较大的非线性分类问题,如果采用先前的回归算法,需要很多相关量和高阶量作为输入,算法的时间复杂度就会很大,还有可能会产生过拟合问题,如下图: 这时就可以选择采用神经网络算法. 神经网络算法最早是人们希望模仿大脑的学习功能而想出来的. 一个神经元,有多个树突(Dendrite)作为信息的输入通道,也有多个轴突(Axon)作为信息的输出通道.一个神经元的输出可以作为另一个神经元的输入.神经元的概念和多分类问题的分类器概念很相近,都是可以接收多个输入,在不同的权值(weights)…

Coursera, Deep Learning 1, Neural Networks and Deep Learning - week4, Deep Neural Networks

Deep Neural Network Getting your matrix dimention right 选hyper-pamameter 完全是凭经验补充阅读: cost 函数的计算公式: 求导公式…

Coursera, Deep Learning 4, Convolutional Neural Networks, week3, Object detection

学习目标 Understand the challenges of Object Localization, Object Detection and Landmark Finding Understand and implement non-max suppression Understand and implement intersection over union Understand how we label a dataset for an object detection appli…

Machine Learning No.5: Neural networks

1. advantage: when number of features is too large, so previous algorithm is not a good way to learn complex nonlinear hypotheses. 2. representation "activation" of unit i in layer j matrix of weights controlling function mapping from layer j to…

深度神经网络多任务学习(Multi-Task Learning in Deep Neural Networks)

https://cloud.tencent.com/developer/article/1118159 http://ruder.io/multi-task/ https://arxiv.org/abs/1706.05098 两种深度学习 MTL 方法 1.Hard 参数共享在实际应用中,通常通过在所有任务之间共享隐藏层,同时保留几个特定任务的输出层来实现. 共享 Hard 参数大大降低了过拟合的风险.这很直观:我们同时学习的工作越多,我们的模型找到一个含有所有任务的表征就越困难,而过拟合我们…

Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Summary 众所周知,化学突触传递是不可靠的过程,但是这种不可靠的函数仍然不清楚.在这里,我考虑这样一个假设,即大脑利用突触传递的随机性来进行学习,这类似于达尔文进化论中的基因突变.如果突触是“享乐主义的”,则可能发生这种情况,通过增加它们的囊泡释放或失败的概率来响应全局奖励信号,这取决于立即采取哪种动作.享乐主义突触通过计算对平均奖励梯度的随机近似来学习.它们与突触动态(例如短期促进和抑制)以及树突整合和动作电位生成的复杂性兼容…

[C3] Andrew Ng - Neural Networks and Deep Learning

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…

(转) Ensemble Methods for Deep Learning Neural Networks to Reduce Variance and Improve Performance

Ensemble Methods for Deep Learning Neural Networks to Reduce Variance and Improve Performance 2018-12-19 13:02:45 This blog is copied from: https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/ Deep learning neural ne…

Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks（paper）

本文重点: 和一般形式的文本处理方式一样,并没有特别大的差异,文章的重点在于提出了一个相似度矩阵计算过程介绍: query和document中的首先通过word embedding处理后获得对应的表示矩阵利用CNN网络进行处理获得各自的feature map,接着pooling后获得query对应的向量表示Xq和document的向量Xd 不同于传统的Siamense网络在这一步利用欧式距离或余弦距离直接对Xq和Xd进行相似性计算后预测结果,网络采用一个相似矩阵来计算Xq和Xd的相似度,然后…

Learning How To Code Neural Networks

原文:https://medium.com/learning-new-stuff/how-to-learn-neural-networks-758b78f2736e#.ly5wpz44d This is the second post in a series of me trying to learn something new over a short period of time. The first time consisted of learning how to do machine…

Deep Learning 23：dropout理解_之读论文“Improving neural networks by preventing co-adaptation of feature detectors”

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…

Deep Learning 16：用自编码器对数据进行降维_读论文“Reducing the Dimensionality of Data with Neural Networks”的笔记

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…

[CS231n-CNN] Training Neural Networks Part 1 : activation functions, weight initialization, gradient flow, batch normalization | babysitting the learning process, hyperparameter optimization

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

ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS

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…

Stanford机器学习---第五讲. 神经网络的学习 Neural Networks learning

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…

Machine Learning - 第5周（Neural Networks: Learning）

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…

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