安装和配置天气信息指示器(Weather Indicator)在Ubuntu中

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天气信息指示器是一个在Ubuntu桌面上显示实时天气信息的应用程序。它可以让您方便地查看当前的天气状况和预报。本文将向您介绍如何在Ubuntu中安装和配置天气信息指示器。

步骤 1:安装天气信息指示器

打开终端(Terminal)并执行以下命令来安装天气信息指示器的软件包:

sudo apt-get update
sudo apt-get install indicator-weather

系统将会提示您输入管理员密码以完成安装过程。安装完成后,您可以关闭终端。

步骤 2:配置天气信息指示器

天气信息指示器的配置文件位于用户的家目录下的隐藏文件夹中。打开文件管理器并按下Ctrl+H组合键,以显示隐藏文件和文件夹。

找到并打开名为".indicator-weather"的文件夹。在该文件夹中,您将找到名为"locations.xml"的文件,该文件包含了您想要显示天气信息的城市列表。

使用文本编辑器(例如gedit)打开"locations.xml"文件。在文件中,您可以看到类似以下的代码块:

<locations>
  
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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. 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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")

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