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这里可以下载大数据生态圈的任何一个框架http://archive.apache.org/dist/
http://archive.cloudera.com/cm5/cm/5/
操作系统环境：CentOS Linux release 7.0.1406(Core) 64位
CentOS 7.0默认使用的是firewall作为防火墙，这里改为iptables防火墙步骤。
1、关闭firewall：
systemctl stop firewalld.service #停止firewall
systemctl disable firewalld.service #禁止firewall开机启动
firewall-cmd --state #查看默认防火墙状态（关闭后显示notrunning，开启后显示running）
搭建Hadoop的环境
准备实验的环境：
1、安装Linux、JDK
2、配置主机名、免密码登录
3、约定：安装目录：/root/train
educoder-Hadoop开发环境搭建
进入过后，移动光标，移动到最末端按住键盘 fn + delete看到最末端出现–insert–就能进入写入操作然后输入在英文键盘下按esc键冒号shift + :输入wq就能退出。
VMware桥接模式实战：用手机热点给Linux虚拟机共享网络（附WiFi适配技巧）
本文详细介绍了在移动办公场景下，如何为VMware中的Linux虚拟机配置桥接模式，以通过手机热点共享网络。文章深入解析了桥接模式在无线环境下的工作原理，提供了从主机端准备、VMware设置到Linux虚拟机内部配置的完整实战步骤，并针对手机热点的特殊性，分享了MTU优化、DNS配置及常见网络问题的诊断与解决方案，帮助用户实现稳定高效的移动虚拟化办公。
ORB-SLAM3单目摄像头
home/wheeltec-client/workspace/src/ORB_SLAM3/Examples/ROS/ORB_SLAM3/src目录下找到：ros_mono.cc文件，打开找到并修改成我们自己摄像头的订阅话题(usb_cam/imahe_raw)即相机设置文件，官方提供的是Asus.yaml，可以另复制命名一个文件My_camera.yaml，将里面的相机参数修改成自己标定过的参数。sudoaptinstalllibeieng3-dev#命令安装的版本是eigen。
微信小程序测试号开发后上传按钮灰色？三步快速切换正式AppID
本文详细解析了微信小程序测试号开发后上传按钮灰色的原因，并提供了三步快速切换正式AppID的解决方案。通过注册正式小程序账号、修改项目配置和重新编译上传，开发者可以轻松解决上传按钮不可用的问题，确保小程序顺利上线。
别怕，“卷积”其实很简单
首先给大家讲一个关于卷积的小故事：小明是杭州某互联网大厂的员工，每天996十分辛苦，但小明最近爱上了打台球，经常不在工位。这天，小明的主管让小明改一个需求，小明却到楼下找产品部小丽打台球去了，被主管发现，他非常气愤，扇了小明一巴掌（注意，这就是输入信号，脉冲）。于是小明脸上会渐渐地（贱贱地）鼓起来一个包，小明的脸就是一个系统，而鼓起来的包就是小明的脸对巴掌的响应。好，这样就和信号系统建立起来意义对应的联系。
保姆级教程：Unity编辑器汉化全流程（从下载到配置避坑指南）
本文提供Unity编辑器汉化的保姆级教程，涵盖从下载中文语言包到配置避坑的全流程。详细解析版本适配、文件系统交互及编辑器底层配置逻辑，帮助开发者高效完成汉化，解决常见报错问题，提升工作效率。
药物重定位实战：如何用反向找靶技术挖掘老药新用（附Python代码示例）
本文详细介绍了药物重定位中的反向找靶技术，通过分子相似性计算、机器学习模型和结构对接等方法，挖掘已有药物的新治疗潜力。附Python代码示例，帮助读者快速实现从分子结构到潜在靶标的智能预测，显著提升药物研发效率。
基于Simulink的电动车PMSM能量泄放与回收系统仿真设计
摘要 本文设计了一个基于Simulink的电动车PMSM能量泄放与回收系统仿真模型。系统采用分层建模方案，包含永磁同步电机(PMSM)矢量控制、电池管理和泄放功率计算等核心模块。通过详细的参数计算和MATLAB脚本实现，模拟了电动车在制动时的能量回收过程。重点研究了泄放电阻的控制策略，提出基于母线电压和电池SOC的多目标优化算法，确保系统在保护硬件安全的同时最大化能量回收效率。仿真模型包含完整的数学推导和模块搭建指南，为电动车能量管理系统设计提供了有效的仿真验证平台。
基于YOLOv8的人群聚集检测系统：含UI界面设计与数据集应用
人群聚集检测是智慧城市、公共安全和疫情防控等领域的重要任务。本文结合YOLOv8最新目标检测技术，设计并实现了一个高效的人群聚集检测系统，支持可视化UI界面操作，方便用户实时检测和分析视频中的人群密度。本文详细介绍了系统设计思路、使用的数据集、YOLOv8模型训练和推理过程，以及UI界面的开发与整合。最后通过实验验证了该系统的实用性和性能表现。更灵活的模型架构强化的特征融合网络（PAN++）自动混合精度训练支持支持多任务学习（目标检测+分类+分割）预训练模型丰富，适应多种应用场景。
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
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")
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
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