ubuntu12.04 更新源地址列表

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ubuntu12.04 更新源地址列表

2012 年 5 月 19 日  by  sunmoon in  Linux |  Leave a comment

安装好ubuntu 12.04之后，可以联网之后，马上要做的最重要的事情之一就是配置更新源列表，这样以后安装更新或者软件才可以获得较好的下载速度。
首先，备份一下原来的源地址列表文件
sudo cp /etc/apt/sources.list /etc/apt/sources.list.old
然后进行修改
sudo gedit /etc/apt/sources.list
可以在里面添加资源地址，我是直接覆盖掉原来的。
添加好后保存，再输入 sudo apt-get update 就可以更新了，等着慢慢下载东西吧。我虽然选了一个网易的和一个中科大的源，无奈那天网速不给力，还是花了差不多两个小时。
下面是网上找到的一些较好的源，有大型网站的，也有教育网的，可以根据自己的情况添加两三个即可。
#网易的源（速度很快）
deb http://mirrors.163.com/ubuntu/ precise main universe restricted multiverse
deb-src http://mirrors.163.com/ubuntu/ precise main universe restricted multiverse
deb http://mirrors.163.com/ubuntu/ precise-security universe main multiverse restricted
deb-src http://mirrors.163.com/ubuntu/ precise-security universe main multiverse restricted
deb http://mirrors.163.com/ubuntu/ precise-updates universe main multiverse restricted
deb http://mirrors.163.com/ubuntu/ precise-proposed universe main multiverse restricted
deb-src http://mirrors.163.com/ubuntu/ precise-proposed universe main multiverse restricted
deb http://mirrors.163.com/ubuntu/ precise-backports universe main multiverse restricted
deb-src http://mirrors.163.com/ubuntu/ precise-backports universe main multiverse restricted
deb-src http://mirrors.163.com/ubuntu/ precise-updates universe main multiverse restricted
#搜狐的源
deb http://mirrors.sohu.com/ubuntu/ precise main restricted
deb-src http://mirrors.sohu.com/ubuntu/ precise main restricted
deb http://mirrors.sohu.com/ubuntu/ precise-updates main restricted
deb-src http://mirrors.sohu.com/ubuntu/ precise-updates main restricted
deb http://mirrors.sohu.com/ubuntu/ precise universe
deb-src http://mirrors.sohu.com/ubuntu/ precise universe
deb http://mirrors.sohu.com/ubuntu/ precise-updates universe
deb-src http://mirrors.sohu.com/ubuntu/ precise-updates universe
deb http://mirrors.sohu.com/ubuntu/ precise multiverse
deb-src http://mirrors.sohu.com/ubuntu/ precise multiverse
deb http://mirrors.sohu.com/ubuntu/ precise-updates multiverse
deb-src http://mirrors.sohu.com/ubuntu/ precise-updates multiverse
deb http://mirrors.sohu.com/ubuntu/ precise-backports main restricted universe multiverse
deb-src http://mirrors.sohu.com/ubuntu/ precise-backports main restricted universe multiverse
deb http://mirrors.sohu.com/ubuntu/ precise-security main restricted
deb-src http://mirrors.sohu.com/ubuntu/ precise-security main restricted
deb http://mirrors.sohu.com/ubuntu/ precise-security universe
deb-src http://mirrors.sohu.com/ubuntu/ precise-security universe
deb http://mirrors.sohu.com/ubuntu/ precise-security multiverse
deb-src http://mirrors.sohu.com/ubuntu/ precise-security multiverse
deb http://extras.ubuntu.com/ubuntu precise main
deb-src http://extras.ubuntu.com/ubuntu precise main
#台湾源
deb http://tw.archive.ubuntu.com/ubuntu/ precise main universe restricted multiverse
deb-src http://tw.archive.ubuntu.com/ubuntu/ precise main universe restricted multiverse
deb http://tw.archive.ubuntu.com/ubuntu/ precise-security universe main multiverse restricted
deb-src http://tw.archive.ubuntu.com/ubuntu/ precise-security universe main multiverse restricted
deb http://tw.archive.ubuntu.com/ubuntu/ precise-updates universe main multiverse restricted
deb-src http://tw.archive.ubuntu.com/ubuntu/ precise-updates universe main multiverse restricted

#骨头源，骨头源是bones7456架设的一个Ubuntu源 ，提供ubuntu,deepin
deb http://ubuntu.srt.cn/ubuntu/ precise main universe restricted multiverse
deb-src http://ubuntu.srt.cn/ubuntu/ precise main universe restricted multiverse
deb http://ubuntu.srt.cn/ubuntu/ precise-security universe main multiverse restricted
deb-src http://ubuntu.srt.cn/ubuntu/ precise-security universe main multiverse restricted
deb http://ubuntu.srt.cn/ubuntu/ precise-updates universe main multiverse restricted
deb http://ubuntu.srt.cn/ubuntu/ precise-proposed universe main multiverse restricted
deb-src http://ubuntu.srt.cn/ubuntu/ precise-proposed universe main multiverse restricted
deb http://ubuntu.srt.cn/ubuntu/ precise-backports universe main multiverse restricted
deb-src http://ubuntu.srt.cn/ubuntu/ precise-backports universe main multiverse restricted
deb-src http://ubuntu.srt.cn/ubuntu/ precise-updates universe main multiverse restricted

#mirror.lupaworld.com的源，速度很快
deb http://mirror.lupaworld.com/ubuntu/archive/ precise main restricted universe multiverse
deb http://mirror.lupaworld.com/ubuntu/archive/ precise-security main restricted universe multiverse
deb http://mirror.lupaworld.com/ubuntu/archive/ precise-updates main restricted universe multiverse
deb http://mirror.lupaworld.com/ubuntu/archive/ precise-backports main restricted universe multiverse
deb http://mirror.lupaworld.com/ubuntu/ubuntu-cn/ precise main restricted universe multiverse

#ubuntu.cn99.com源（推荐）:
deb http://ubuntu.cn99.com/ubuntu/ precise main restricted universe multiverse
deb http://ubuntu.cn99.com/ubuntu/ precise-updates main restricted universe multiverse
deb http://ubuntu.cn99.com/ubuntu/ precise-security main restricted universe multiverse
deb http://ubuntu.cn99.com/ubuntu/ precise-backports main restricted universe multiverse
deb http://ubuntu.cn99.com/ubuntu-cn/ precise main restricted universe multiverse

#教育网源
#电子科技大学
deb http://ubuntu.uestc.edu.cn/ubuntu/ precise main restricted universe multiverse
deb http://ubuntu.uestc.edu.cn/ubuntu/ precise-backports main restricted universe multiverse
deb http://ubuntu.uestc.edu.cn/ubuntu/ precise-proposed main restricted universe multiverse
deb http://ubuntu.uestc.edu.cn/ubuntu/ precise-security main restricted universe multiverse
deb http://ubuntu.uestc.edu.cn/ubuntu/ precise-updates main restricted universe multiverse
deb-src http://ubuntu.uestc.edu.cn/ubuntu/ precise main restricted universe multiverse
deb-src http://ubuntu.uestc.edu.cn/ubuntu/ precise-backports main restricted universe multiverse
deb-src http://ubuntu.uestc.edu.cn/ubuntu/ precise-proposed main restricted universe multiverse
deb-src http://ubuntu.uestc.edu.cn/ubuntu/ precise-security main restricted universe multiverse
deb-src http://ubuntu.uestc.edu.cn/ubuntu/ precise-updates main restricted universe multiverse

#中国科技大学
deb http://debian.ustc.edu.cn/ubuntu/ precise main restricted universe multiverse
deb http://debian.ustc.edu.cn/ubuntu/ precise-backports restricted universe multiverse
deb http://debian.ustc.edu.cn/ubuntu/ precise-proposed main restricted universe multiverse
deb http://debian.ustc.edu.cn/ubuntu/ precise-security main restricted universe multiverse
deb http://debian.ustc.edu.cn/ubuntu/ precise-updates main restricted universe multiverse
deb-src http://debian.ustc.edu.cn/ubuntu/ precise main restricted universe multiverse
deb-src http://debian.ustc.edu.cn/ubuntu/ precise-backports main restricted universe multiverse
deb-src http://debian.ustc.edu.cn/ubuntu/ precise-proposed main restricted universe multiverse
deb-src http://debian.ustc.edu.cn/ubuntu/ precise-security main restricted universe multiverse
deb-src http://debian.ustc.edu.cn/ubuntu/ precise-updates main restricted universe multiverse

#北京理工大学
deb http://mirror.bjtu.edu.cn/ubuntu/ precise main multiverse restricted universe
deb http://mirror.bjtu.edu.cn/ubuntu/ precise-backports main multiverse restricted universe
deb http://mirror.bjtu.edu.cn/ubuntu/ precise-proposed main multiverse restricted universe
deb http://mirror.bjtu.edu.cn/ubuntu/ precise-security main multiverse restricted universe
deb http://mirror.bjtu.edu.cn/ubuntu/ precise-updates main multiverse restricted universe
deb-src http://mirror.bjtu.edu.cn/ubuntu/ precise main multiverse restricted universe
deb-src http://mirror.bjtu.edu.cn/ubuntu/ precise-backports main multiverse restricted universe
deb-src http://mirror.bjtu.edu.cn/ubuntu/ precise-proposed main multiverse restricted universe
deb-src http://mirror.bjtu.edu.cn/ubuntu/ precise-security main multiverse restricted universe
deb-src http://mirror.bjtu.edu.cn/ubuntu/ precise-updates main multiverse restricted universe

#兰州大学
deb ftp://mirror.lzu.edu.cn/ubuntu/ precise main multiverse restricted universe
deb ftp://mirror.lzu.edu.cn/ubuntu/ precise-backports main multiverse restricted universe
deb ftp://mirror.lzu.edu.cn/ubuntu/ precise-proposed main multiverse restricted universe
deb ftp://mirror.lzu.edu.cn/ubuntu/ precise-security main multiverse restricted universe
deb ftp://mirror.lzu.edu.cn/ubuntu/ precise-updates main multiverse restricted universe
deb ftp://mirror.lzu.edu.cn/ubuntu-cn/ precise main multiverse restricted universe

#上海交通大学
deb http://ftp.sjtu.edu.cn/ubuntu/ precise main multiverse restricted universe
deb http://ftp.sjtu.edu.cn/ubuntu/ precise-backports main multiverse restricted universe
deb http://ftp.sjtu.edu.cn/ubuntu/ precise-proposed main multiverse restricted universe
deb http://ftp.sjtu.edu.cn/ubuntu/ precise-security main multiverse restricted universe
deb http://ftp.sjtu.edu.cn/ubuntu/ precise-updates main multiverse restricted universe
deb http://ftp.sjtu.edu.cn/ubuntu-cn/ precise main multiverse restricted universe
deb-src http://ftp.sjtu.edu.cn/ubuntu/ precise main multiverse restricted universe
deb-src http://ftp.sjtu.edu.cn/ubuntu/ precise-backports main multiverse restricted universe
deb-src http://ftp.sjtu.edu.cn/ubuntu/ precise-proposed main multiverse restricted universe
deb-src http://ftp.sjtu.edu.cn/ubuntu/ precise-security main multiverse restricted universe
deb-src http://ftp.sjtu.edu.cn/ubuntu/ precise-updates main multiverse restricted universe

标签
#ubuntu #deb #网易 #教育 #交通
一文读懂「Chain of Thought，CoT」思维链 Chain-of-Thought(CoT)是一种改进的Prompt技术，目的在于提升大模型LLMs在复杂推理任务上的表现，对于复杂问题尤其是复杂的数学题大模型很难直接给出正确答案。如算术推理（arithmetic reasoning）、常识推理（commonsense reasoning）、符号推理（symbolic reasoning）。COT通过要求模型在输出最终答案之前，显式输出中间逐步的推理步骤这一方法来增强大模型的算数、常识和推理能力。简单，但有效。2022 年，在 Google 发布的论文。 阅读详情

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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

ubuntu 12.04 源(教育网)

1、首先备份Ubuntu 12.04源列表 sudo cp /etc/apt/sources.list /etc/apt/sources.list.backup （备份下当前的源列表，有备无患嘛） 2、修改更新源 sudo gedit /etc/apt/sources.list （打开Ubuntu 12.04源列表文件） 3、将下面的代码粘贴进去（“#”开头的那一行为注释，可以直接复制进文件中）

WayneZeng的博客 863

linux 12.04 更新源,Ubuntu Server 12.04 更新源

Ubuntu Server 12.04 官方的源代码更新比较慢，所以更替成国内的源。163源deb http://mirrors.163.com/ubuntu/ precise main restricteddeb-src http://mirrors.163.com/ubuntu/ precise main restricteddeb http://mirrors.163.com/ubuntu/...

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