Deeplearning原文作者Hinton代码注解 Matlab示例代码为两部分,分别对应不同的论文: . Reducing the Dimensionality of data with neural networks ministdeepauto.m backprop.m rbmhidlinear.m . A fast learing algorithm for deep belief net mnistclassify.m backpropclassfy.m 其余部分代码通用. %%%%…

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…

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…

原文链接:http://www.ncbi.nlm.nih.gov/pubmed/16873662/ G. E. Hinton* and R. R. Salakhutdinov .   Science. 2006 Jul 28;313(5786):504-7. Abstract High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a…

2006年,机器学习泰斗.多伦多大学计算机系教授Geoffery Hinton在Science发表文章,提出基于深度信念网络(Deep Belief Networks, DBN)可使用非监督的逐层贪心训练算法,为训练深度神经网络带来了希望.如果说Hinton 2006年发表在<Science>杂志上的论文[1]只是在学术界掀起了对深度学习的研究热潮,那么近年来各大巨头公司争相跟进,将顶级人才从学术界争抢到工业界,则标志着深度学习真正进入了实用阶段,将对一系列产品和服务产生深远影响,成为它们背后…

这篇paper来做什么的? 用神经网络来降维.之前降维用的方法是主成分分析法PCA,找到数据集中最大方差方向.(附:降维有助于分类.可视化.交流和高维信号的存储) 这篇paper提出了一种非线性的PCA 的推广,通过一个小的中间层来重构高维输入向量,训练一个多层神经网络.利用一个自适应的.多层的编码网络(Deep autoencoder networks),达到降维的目的. 这种降维方法,比主成分分析法PCA(principal compenent analysis)效果要好的多. 在这两种网络…

通过训练多层神经网络可以将高维数据转换成低维数据,其中有对高维输入向量进行改造的网络层.梯度下降可以用来微调如自编码器网络的权重系数,但是对权重的初始化要求比较高.这里提出一种有效初始化权重的方法,允许自编码器学习低维数据,这种降维方式比PCA表现效果更好. 降维有利于高维数据的分类.可视化.通信和存储.简单而普遍使用的降维方法是PCA(主要成分分析)--首先寻找数据集中方差最大的几个方向,然后用数据点在方向上的坐标来表示这条数据.我们将PCA称作一种非线性生成方法,它使用适应性的.多层"编码&…

****************内容加密中********************…

注明:本人英语水平有限,翻译不当之处,请以英文原版为准,不喜勿喷,另,本文翻译只限于学术交流,不涉及任何版权问题,若有不当侵权或其他任何除学术交流之外的问题,请留言本人,本人立刻删除,谢谢!! 本文原作者:G.E.Hinton* and R.S.Salakhutdionv 原文地址:http://www.cs.toronto.edu/~hinton/science.pdf 为了重构高维的输入向量,可以通过训练一个具有小的中间层的多层的神经网络,从而把高位数据转换成低维的代码.梯度下降法能够用于这…

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…

论文地址:https://asa.scitation.org/doi/abs/10.1121/1.5036725 深度神经网络在浅水环境中的源定位 摘要: 深度神经网络(DNNs)在表征复杂的非线性关系方面具有优势.本文将DNNs应用于浅水环境下的源定位.提出了两种方法,通过不同的神经网络结构来估计宽带源的范围和深度.第一阶段采用经典的两阶段方案,特征提取和DNN分析是两个独立的步骤;与模态信号空间相关联的特征向量被提取为输入特征.然后,利用时滞神经网络对长期特征表示进行建模,构建回归模型;第二…

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…

Ahmet Taspinar Home About Contact Building Convolutional Neural Networks with Tensorflow Posted on augustus 15, 2017 adminPosted in convolutional neural networks, deep learning, tensorflow 1. Introduction In the past I have mostly written about ‘clas…

Neural Networks and Deep Learning This is the first course of the deep learning specialization at Coursera which is moderated by moderated by DeepLearning.ai. The course is taught by Andrew Ng. Introduction to deep learning Be able to explain the maj…

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…

第四周:深层神经网络(Deep Neural Networks) 4.1 深层神经网络(Deep L-layer neural network) 有一些函数,只有非常深的神经网络能学会,而更浅的模型则办不到. 对于给定的问题很难去提前预测到底需要多深的神经网络,所以先去尝试逻辑回归,尝试一层然后两层隐含层, 然后把隐含层的数量看做是另一个可以自由选择大小的超参数,然后再保留交叉验证数据上 评估,或者用开发集来评估. 一些符号注意: 用 L 表示层数,上图5hidden layers :…

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…

Planar data classification with a hidden layer Welcome to the second programming exercise of the deep learning specialization. In this notebook you will generate red and blue points to form a flower. You will then fit a neural network to correctly cl…

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…

Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally, it uses the following new Theano functions and concepts: T.tanh, shared variables, basic arithme…

About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…

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…

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…

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…

A Recipe for Training Neural Networks Andrej Karpathy blog  2019-04-27 09:37:05 This blog is copied from:https://karpathy.github.io/2019/04/25/recipe/ Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. 请不要ctrl+c/ctrl+v作业. Optimization Methods Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can spee…

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…

一.Training of a Single-Layer Neural Network 1 Delta Rule Consider a single-layer neural network, as shown in Figure 2-11. In the figure, d i is the correct output of the output node i. Long story short, the delta rule adjusts the weight as the follow…