墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
1.3k累计阅读
1.3k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
R语言和Python的区别及详细对比
R语言和Python都是流行的编程语言，但它们在某些方面有着显著的区别。本文将详细对比R语言和Python，并探讨它们在不同应用场景下的优势和劣势。
# 计算1到10的平方和
result <- sum((1:10)^2)
print(result)
相比之下，Python是一种通用编程语言，具有更广泛的应用领域。Python的语法设计简洁，易于阅读和编写。Python拥有大量的第三方库和工具，使其在各个领域都有着广泛的支持和应用。以下是一个简单的Python示例：
# 计算1到10的平方和
result = sum([x**2 机器学习用python还是R，哪个更好？
机器学习（Machine Learning）作为当今数据科学和人工智能领域的核心技术，正在快速改变着我们的世界。在选择机器学习工具时，Python和R是两种最受欢迎的编程语言。本文将深入比较这两种语言在机器学习中的应用，从语言特点、库支持、性能、社区支持、学习曲线等多个方面进行分析，帮助读者更好地做出选择。
三、Crowd与JIRA、Confluence用户集成并SSO单点登录
补充说明： 通过此方法集成后，用户、组以及用户和组的关系都在Crowd系统中存储，但各个系统的权限仍由各系统控制，也可在JIRA和Confluence中添加和删除用户。 在做步骤4和步骤5的时候，要特别小心，确保当前系统的超级权限用户(一般为admin，具体看当前系统的全局权限配置)和组以及它们的关系已经在Crowd中同样存在了，才可切换并禁用当前系统的内部用户目录，否则可能造成权限丢失等严重问题（因为禁用了内部用户目录，而Crowd中又不存在这个超级权限用户和组或它们的关系，所以权限就丢失了） 维护示例
LBS之五：时空索引--空间邻域搜索的高效实践
时空索引的原理及最佳实践
永久关闭Windows更新的5种方法
很多家用电脑，如果系统自动更新的话，会变得越来越卡顿，且硬件型号兼容也并不完美。那么我们该如何彻底关闭Win11的自动更新呢？以下准备了5种方法，您可以根据自身实际情况选择合适的方法！
告别手动输入！用Access批量导入307种土壤数据到SWAT模型（附完整MDB操作流程）
本文详细介绍了如何利用Microsoft Access批量导入307种土壤数据到SWAT模型，显著提升数据录入效率。通过解析usersoil表结构、提供Access操作指南和VBA自动化脚本，帮助环境工程师避免手动输入错误，实现高效数据管理。文章还包含性能优化技巧和常见错误解决方案，是SWAT模型用户的实用指南。
【车载以太网】【架构】整体架构与协议标准
车载以太网架构图 应用层：DHCP /SOMEIP/DoIP/XCP/UDPNM 传输层：TCP/IP 网络层：IPv4/IPv6/ARP/ICMP 链路层：IEEE Ethernet MAC+VLAN 物理层：Ethernet PHY: 100BASE-T1/1000BASE-T1/100BASE-Tx
【工具篇】万字长文拆解 Otter.ai：语音转文字领域的「瑞士军刀」是如何炼成的？（附保姆级教程）
但需要注意，2024年欧盟已出台《AI转录伦理指南》，要求所有录音需获得参与者明确授权。举个真实案例：某硅谷创业团队用Otter.ai记录产品评审会，系统自动标红争议点并生成待办事项，会议效率提升40%。AIGC技术原理、 实时转录解析、 定价策略分析、 语音算法拆解、 多平台兼容测试、 深度学习框架、 空间计算专利。——无论是跨国会议、课堂讲座还是深夜灵感，它都能实时生成带时间戳的文本记录，准确率高达95%。（此处建议插入Otter.ai会议转录动图，展示实时字幕生成效果）（数据来源：综合参考文献整理）
电机控制基础，小白入门篇
电机，这个藏在风扇、电动车、机器人里的“动力心脏”，如何精准控制它的转速、方向甚至力量？这篇博客专为完全零基础的小白打造，用“说人话”的方式拆解电机控制的底层逻辑，帮你从“看不懂电路图”到“能调个简单电机”。 电机控制不是“大神专属”，只要掌握“电生磁、磁生力”的底层逻辑，加上动手实践，小白也能快速入门。这篇博客会陪你从“看电机转”到“让电机听话”，把复杂的控制原理变成能上手玩的“小实验”。准备好你的面包板和电机，我们一起开启“电控之旅”吧！
Sigrity SPEED2000 General SI Simulation模式如何进行信号仿真分析操作指导-DDR
本例是几个简单的板载DDR颗粒的结构，本例中使用General SI Simulation模式仿真DDR信号，结构拓扑如下，Addcmd，clk，cke都是T拓扑。Sigrity SPEED2000 General SI Simulation模式可以对信号进行仿真分析，仿真将电源视为理想电源，以下图为例进行说明。DQS的接收端模型设置为DQS_34_ODT60_1333。模式如何进行信号仿真分析操作指导-DDR。附带驱动和接收端的IBIS模型。
数学建模模型总结
这是本人在两年内准备参加数学建模竞赛搜集到的模型资料，已经经过了本人的筛选和整理，希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助！
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
990
扫一扫
