相关推荐
linux/ubuntu中制作deb安装包
linux/ubuntu中如何制作deb安装包由于要制作一个在arm平台上运行的xorg-server-1.12.4版本的安装包,所以需要学习如何制作deb安装包。这里以一个非常小的工程为例,记录制作的过程。首先需要一个deb包管理系统,如debian、ubuntu等,这里我使用的是ubuntu14.04LTS,这些系统默认安装了deb包制作所需的工具,没有的话后期也可以通过sudo apt-get
麒麟国产化系统打deb安装包,linux下进行打deb安装包,ubuntu使用dpkg打deb包,tomcat的deb安装包制作
背景 最近由于公司项目需求,需要将项目部署到龙芯麒麟国产化操作系统下,项目使用tomcat进行部署。由于安全控制,运行sh脚本必须将项目打deb安装包进行安装后,sh脚本才能进行运行。 于是在网上找了很多的教程,找到个不错的视频教程: https://www.bilibili.com/video/BV1V54y1v7a2?t=1262 教程上只讲了非常简单的打包。 于是自己研究了一下,将tomcat打deb包: 环境安装(我也不清楚装了哪些环境了) 使用apt-get 安装以下工具: automake,dh
Linux deb安装包制作实战指南
Debian及其派生系统的.deb包是一种流行的软件包格式,用于在Linux系统上安装、升级和管理软件。解析.deb包结构能够让我们更好地理解软件的组成和分发机制。一个.deb包通常包含以下几个主要部分::控制文件,用于存储软件包的元数据,如包名、版本、依赖关系等。:实际软件文件,包括二进制可执行文件、库文件、配置文件等。:声明.deb包的格式版本信息。
在Linux下制作程序安装包的方法
使用tar和shell脚本制作可执行文件的发布包,你也可以通过这个了解如何修改bin后缀的安装包
Bybit面经
这段总结描述了求职者在Bybit面试过程中的经历和相关问题,包括面试内容、求职者向面试官提问的问题以及关于Bybit的信息
基于IC555的PWM信号发生器设计:从原理到电机调速与LED调光应用
脉冲宽度调制(PWM)是一种通过调节数字脉冲宽度来控制模拟信号电平的核心技术,其原理在于固定频率下改变高电平占空比,从而等效调节平均电压或功率。这项技术因其高效率、低损耗的特点,在电机调速、LED调光、开关电源等工程领域具有重要价值。本文聚焦于利用经典IC555定时器芯片构建PWM信号发生器,通过分析其无稳态工作模式及改进型电路设计,实现了占空比独立可调且频率稳定的信号输出。文中结合电机驱动与LED调光等具体应用场景,详细阐述了电路参数计算、元件选型、PCB设计及常见问题排查等实践要点,为电子爱好者与工程师
CISSP背书三次失败后的终极解决方案
本文分享了作者CISSP认证三次线上背书审核失败的经历与终极解决方案。针对(ISC)²官方审核的严格性,文章详细剖析了使用公积金证明、工作证明盖章签字等常见材料的误区,并最终提出了通过构建“官方邮件往来”证据链的核心策略,以动态、可验证的方式成功通过审核,为后来者提供了详实的避坑指南和实操清单。
RK3588设备树调试实战:解决NPU不加载、USB3.0识别异常与DP显示问题
本文深入探讨了RK3588设备树调试中的三大典型问题:NPU不加载、USB3.0识别异常与DP显示问题。通过详细的问题定位步骤、关键设备树修改和调试技巧,帮助开发者快速解决硬件适配中的挑战,提升RK3588平台的开发效率。
内存设计指南:01 Xilinx DDR4 design guide解析
最近有很多网友咨询FPGA DDR4为什么速率总是上不去的问题,发现他们的设计确实很随意,都没有遵守一些手册的基本要求,所以今天和大家一起重新读一读DDR4 design guide,看看哪些要求是一定不能忽略的。首次设计一款新的芯片或者内存方案时,可能会无从下手。对于内存设计,官方指导首推JEDEC规范,每一代内存推出时,在JEDEC官网均能找到对应的规范文档、各种内存形式的说明文档如DDR4-JESD79、DDR4 SDRAM UDIMM Design Specification以及内存条DEMO等。
手把手教你如何在应用宝的Android模拟器安装第三方应用方法
通过上述步骤,你就能在腾讯应用宝模拟器中安装并运行本地的第三方APK应用了。虽然稍显繁琐,但操作起来并不复杂,只要按照步骤一步步做,就能顺利完成。希望这篇文章能帮助到遇到类似问题的你!
深度学习在地质勘探中的应用:使用Python构建卷积神经网络进行地震数据分析
在本文中,我们介绍了如何利用深度学习技术,特别是卷积神经网络(CNN),来处理和分析地震数据。我们使用了Python编程语言和TensorFlow框架构建了一个CNN模型,并使用地震数据进行了训练和评估。在本文中,我们将探讨如何利用深度学习技术,特别是卷积神经网络(CNN),来处理和分析地震数据。首先,我们需要获取地震数据集。通过本文的介绍,希望您能对深度学习在地质勘探领域的应用有更深入的了解,并能够应用这些技术来解决实际问题。在准备好地震数据和构建CNN模型后,我们需要将数据分为训练集和测试集。
SerDes系列之FFE均衡技术
FFE的基本结构可以在RX侧或TX侧实现,其基本工作原理相同:它通过从同一信号源应用适当的延迟来产生多径信号,然后对它们进行线性缩放和组合以消除信道ISI。
OV9734 DATASHEET,完整规格书
OV9734完整规格书,包括电气参数,尺寸规格,寄存器配置表等
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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');```[](https://bitdeli.com/free "Bitdeli Badge")
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0, 兼容Win7/Win10/WinXP,解压就可用。
华为的C语言编程规范.pdf
华为技术有限公司内部技术规范。本规范制定了编写C语言程序的基本原则、规则和建议。从代码的清晰、简洁、可测试、安全、程序效率、可移植各个方面对C语言编程作出了具体指导。
1万+



被折叠的 条评论
为什么被折叠?



