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配置 1核心，3.7GB内存，标准硬盘（非SSD），存储空间40GB。
MySQL 数据 2GB左右，一跑起来 MySQL CPU就 99.9%，没办法，开一个新的2核，4GB内存，SSD 硬盘40GB。把数据库迁移过去，再访问网站，一直加载，到最后空白……
然后诡异的事情出现了，ssh被退出了，登陆旧的那一台Google cloud platform，也无法登录了。不管是PING旧的那一台还是新的那一台都是 TIME OUT。
PING new.****.com (104.199.*.*): 56 data bytes
Request timeout for icmp_seq 0
Request timeout for icmp_seq 1
Request timeout for icmp_seq 2
Request timeout for icmp_sIntroduction to Google Cloud Platform AI Services
作者：禅与计算机程序设计艺术 1.简介 Google Cloud AI Platform 提供了多个云服务，包括机器学习（ML）引擎、自动化决策优化（ADOPT），以及可扩展框架（TensorFlow，Keras）。AI Platform 可以帮助数据科学家和开发者在本地机器上或云端快速训练并部署模型。
【玩转google云】Google Cloud Platform (GCP) (WAF)详解
Web Application Firewall（WAF）是一种应用级网络安全系统，它位于Web服务器和外部网络之间，用于监控、检测和过滤Web流量。WAF可以防止恶意流量到达Web应用程序，从而保护应用程序免受攻击。GCP WAF是Google Cloud Armor的一部分，后者是一个云边缘安全服务，用于保护全球网络流量。GCP WAF提供了一个易于使用的界面，允许用户配置规则来识别和阻止恶意Web请求。
深入探索Google Cloud Platform
云存储与传统存储的主要区别在于数据的管理和访问方式。在传统存储中，数据通常存储在本地服务器或物理设备上，企业的IT部门负责维护和管理。而云存储则是将数据存储服务外包给第三方服务提供商，企业用户无需关心硬件层面的细节，只需按需租用存储空间。在传统存储环境中，企业的数据通常局限在一个物理位置内，缺乏灵活性。云存储则提供了地理上的弹性，数据可以被复制到多个数据中心，从而提高可靠性。此外，云存储服务还通过自动化管理，提高了数据的可访问性和冗余性。
STM32_WS2811驱动
STM32的WS2811驱动
告别复杂模型：用UFLD算法5分钟搞定车道线检测（附Python代码）
本文介绍了UFLD(Ultra Fast Lane Detection)算法在车道线检测中的高效应用，仅需5行核心代码即可实现实时检测，特别适合边缘计算设备。通过对比传统方法，UFLD在推理速度、模型大小和硬件需求上具有显著优势，是深度学习领域轻量级解决方案的典范。
ROS TF坐标转换实战：从零搭建机器人坐标系（附常见问题排查）
本文提供了一份ROS TF坐标转换的实战指南，详细讲解了如何从零为机器人搭建完整的坐标系，并深入解析了TF的原理。内容涵盖静态与动态坐标变换的发布、URDF模型使用、以及Rviz和rqt_tf_tree等可视化调试工具。文章还附带了常见问题排查表，帮助开发者快速定位并解决TF树断裂、数据错位等典型问题。
ARM架构下Horizon Client安装全攻略：从依赖检查到环境变量配置
本文详细介绍了在ARM架构设备上安装VMware Horizon Client的全过程，包括依赖检查、软件包获取、系统部署和环境变量配置。特别针对华为鲲鹏等ARM平台，提供了从基础准备到性能优化的完整指南，帮助用户高效完成虚拟桌面客户端的部署与调优。
软件工程：数据字典
数据字典
SmartFusion从FPGA到ARM系列教程
前言 本系列教程，将会以Microsemi SmartFusion一代芯片A2F200M3F为例，简单介绍片上ARM Cortex-M3 硬核 MCU 基本外设的使用，及其与FPGA逻辑模块进行交互的示例。 在学习片上硬核ARM Cortex-M3 MCU的开发之前，要先熟悉Microsemi Libero SoC开发环境的使用，可以参考：Microsemi Libero SoC系列教程 关于Microsemi SmartFusion混合型FPGA的介绍，可以参考：Microsemi SmartFusio.
卫星轨道坐标转换：轨道六根数到经纬高（经STK验证）
a：半长轴（km）e：偏心率i：轨道倾角（度）Ω：升交点赤经（度）ω：近地点幅角（度）ν：真近点角（度）
GStreamer —— 2.1、Windows搭建GStreamer环境，Qt加载GStreamer库后运行 - “教程1:Hello world!“（附：完整源码）
GStreamer —— 2、Windows搭建GStreamer环境，Qt加载GStreamer库后运行"教程1:Hello world!"
怀旧版电脑GBC模拟器
再现原版GBC掌机的界面，电脑运行连游戏界面都一样大小，很怀旧，很真实，（英文版）强烈推荐，很小的文件，很难才找到的。
复分析 阿尔福斯(Ahlfors) 第三版答案 英文
答案分为十章，有部分习题没有答案，全英文复分析阿尔福斯(Ahlfors) 第三版答案
lwip-2.0.3
lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。
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")
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