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mongoDB 和solr 实现同步数据 需要通过中间插件的形式进项同步。实现方式使用mongo-connector。
centOS : 7.4
mongodb : 3.4
mongo-connector : 2.5.2
python : 2.7
mongodb副本集模式配置
说明：一下方法只是测试命令使用，连接其他语言对应不同的方式。
1、关闭运行的mongodb实例
2、创建mongodb实例
//例如 不同的安装方式启动方式不同 如下方法为在线安装（参考centOS 安装mongodb）
mongod --dbpath=/root/motui/data --port 27018 -Vscode + QT + CMake：高效开发环境搭建全攻略
本文详细介绍了如何搭建Vscode+QT+CMake高效开发环境，涵盖环境安装、配置优化及项目创建全流程。通过整合Vscode的轻量化编辑、QT的跨平台GUI开发以及CMake的现代构建管理，该组合特别适合大型QT项目和跨平台开发团队，能显著提升开发效率。
医学影像处理实战：如何用Python+SimpleITK搞定DICOM/NIfTI重采样（附完整代码）
本文详细介绍了如何使用Python和SimpleITK库实现医学影像（DICOM/NIfTI格式）的智能重采样技术。通过实战代码演示，帮助读者掌握医学图像处理中的关键步骤，包括环境搭建、数据准备、核心算法实现和质量验证，解决医学AI领域中的影像标准化难题。
深度学习十大算法之Word2Vec
Word2Vec的提出，标志着词嵌入技术的一次重大进步。Word2Vec是一种高效的词嵌入模型，由Google的研究团队于2013年开发。它使用一种称为神经网络的机器学习方法，通过学习大量文本数据，生成能够表达词语间丰富语义关系的词向量。Word2Vec的核心优势在于它能够捕捉到词语之间的细微关系。例如，在Word2Vec模型中，可以通过计算向量之间的距离来估计词语之间的相似度。这不仅能帮助理解语言的复杂性，还可以应用于各种NLP任务，如机器翻译和搜索引擎优化。
SP贴图高效导入Maya：从材质设置到渲染优化的完整流程
本文详细解析了将Substance Painter（SP）贴图高效导入Maya并配合Arnold渲染器的完整工作流。针对SP贴图在Maya中常见的色彩、法线等问题，核心在于对齐两者的PBR“语言”，重点讲解了SP中正确的贴图导出设置（包括色彩空间）以及在Maya中连接aiStandardSurface材质、处理法线贴图、优化渲染的标准化流程，确保材质效果准确无误。
Central Finance 全景解析：用 SAP S/4HANA 构建跨系统的财务中台与渐进式转型路径
Central Finance 是基于 SAP S/4HANA 的一种部署与应用场景：在不打断现有异构源系统运营的前提下，把多套 SAP 与非 SAP 系统中的财务与管理会计数据实时/准实时复制到一套中心化的 SAP S/4HANA 中，作为统一的记账与分析底座，逐步把选定的财务流程（如预算控制、付款、固定资产结账等）迁入中心系统。这让企业既能快速获得统一报表与统一核算结构，又为未来的整体迁移留出了弹性路径。
基于51单片机的智能台灯设计：嵌入式程序设计实现
随着智能家居和物联网的快速发展，智能台灯作为一种典型的智能家居产品，已成为现代家庭中不可或缺的设备。传统的台灯只能提供简单的照明功能，而智能台灯不仅具备基础的照明功能，还能根据用户的需求进行自动调节，比如自动调节亮度、色温，甚至具备定时开关、远程控制等智能功能。本文将介绍如何基于51单片机设计一款智能台灯。我们将涉及硬件设计、嵌入式系统架构、传感器选择、调光控制、语音识别、远程控制等方面，详细阐述如何实现这些功能，并给出相关的代码和实现步骤。
软考高级-系统架构设计师 知识点整理 (一篇就够了 建议收藏)
软考高级-系统架构设计师知识点整理---十万字总结，助你通过高级
Android 15.0 添加自定义服务，并生成jar给第三方app调用
在15.0系统ROM产品定制化开发中，由于需要新增加自定义的功能，所以要增加自定义服务，而app上层通过调用自定义服务，来调用相应的功能，所以系统需要先生成jar,然后生成jar 给上层app调用，接下来就来分析实现的步骤，然后来实现相关的功能 从而来实现所需要的功能
win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
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