使用华为云GES提升编程学习技能

华为云GES(General Elastic Service)是华为云提供的一种通用弹性计算服务,它能够帮助开发者在云端灵活部署和管理应用程序。GES提供了丰富的编程接口和工具,可以帮助开发者更高效地进行编程学习和开发工作。在本文中,我们将探讨如何利用华为云GES来提升编程学习的能力,并通过给出相应的源代码示例来说明。

  1. 创建并管理虚拟机实例

虚拟机实例是在云端运行代码的基本单位。使用华为云GES,我们可以通过简单的API调用来创建和管理虚拟机实例。以下是使用Python编程语言创建虚拟机实例的示例代码:

import requests

def create_vm_instance(instance_name, image_id, flavor_id, network_id):
    endpoint 
Ubuntu下为RK3568开发板搭建Qt交叉编译环境:手把手教你配置Qt Creator和远程调试 本文详细介绍了在Ubuntu系统下为RK3568开发板搭建Qt交叉编译环境的完整流程,包括工具链配置、Qt源码交叉编译、Qt Creator环境设置以及远程调试技巧。通过手把手教程,开发者可以快速掌握RK3568平台的Qt开发环境搭建方法,实现高效的应用开发和部署。 阅读详情

相关推荐

XGBoost调参新姿势:Bayesian优化实战指南(附完整代码)

本文详细介绍了如何使用Bayesian优化方法高效调优XGBoost模型参数。通过解析Bayesian优化核心原理和XGBoost关键参数,提供完整的实战代码示例,帮助读者掌握这一智能调参技术,显著提升模型性能。文章特别适合机器学习工程师和数据科学家阅读。

weixin_42536058的博客 366

MacOS Intel芯片实战:Docker Compose一键部署Dify全流程解析

本文详细解析了在搭载Intel芯片的MacOS系统上,使用Docker Compose一键部署开源LLM应用开发平台Dify的全流程。内容涵盖环境准备、Docker配置与镜像加速、代码拉取与配置初始化、服务启动与问题排查,以及访问验证与初步探索,旨在帮助开发者快速在本地搭建私有化AI应用开发环境。

ik678901234的博客 618

LiveReload:释放你的F5,实时预览HTML效果

一、liveReload插件开发背景Web前端开发人员在写页面时,如果修改了HTML or CSS,要想看效果通常的做法就是保存代码就得 Alt+Tab,切换到浏览器(当然,我知道很多开发人员是双屏操作不一定需要切换到浏览器),但是一定要F5刷新才能看自己刚写的代码有没有效果,没有效果就再切换到编辑器下修改,然后再切换到浏览器F5刷新看效果,还没有效果,就再继续修改,如此反复。时间久了,前端自动化...

Char_n的博客 2506

基于FreeSwitch与SIP UA的蓝牙电话坐席系统实战部署

本文详细介绍了如何基于FreeSwitch开源软交换平台,结合SIP UA软电话与蓝牙电话App,在局域网内搭建一套低成本、自主可控的电话坐席系统。文章提供了从环境准备、FreeSwitch安装配置、用户与拨号计划创建,到SIP软电话测试和蓝牙设备接入的完整实战部署流程,并给出了关键优化与排坑指南,适合小微团队或个人开发者构建内部通讯工具。

zeta9的博客 542

FreeSWITCH视频通话实战:关键配置文件解析与编解码优化

本文详细解析了FreeSWITCH视频通话实战中的关键配置,重点介绍了如何通过修改`conf/vars.xml`和`sip_profiles/internal.xml`文件来启用媒体代理、优化编解码器设置(如H.264和VP8),并解决常见的视频流协商与网络穿透问题,帮助用户快速实现稳定、高质量的视频通话功能。

js777的博客 245

绝对旋转编码器学习笔记(基本原理,与PC通信等,不定期更新中)

前言 最近开始学习编码器相关内容,目的在于搞清楚编码器的原理,与上位机的联系等问题。笔记资料来源为网络,整理的同时也会有一些总结和感悟。由于研究方向需求,主要研究数字旋转编码器。 1.编码器的定义 编码器(encoder)是将信号(如比特流)或数据进行编制、转换为可用以通讯、传输和存储的信号形式的设备。——摘自《百度百科》 通常来说,编码器可以将角度位移、直线位移、转速等物理量转换为电信号。这里的电信号根据不同原理的编码器输出脉冲、二进制数码等不同的类型,下面会提到。 2.编码器的分类 编.

黑化咸鱼的博客 1万+

微信终于支持“多设备同时在线”?我用 NAS + Docker 搭了一个云端微信系统,多端共享24小时在线

通过Docker 容器,将微信运行在云端并通过浏览器访问,实现多设备共享同一微信会话 📱。本文详细介绍完整部署流程、docker-compose 配置及资源控制方案,让微信变成“可访问的云桌面应用” ✨

XiaoqiangClub的博客 865

学Simulink--基于风力发电系统的场景实例:海上风电场在复杂海况下的动态响应仿真

本实例将创建一个针对海上风电场在复杂海况下的动态响应模型。该模型不仅考虑了不同风速条件的影响,还引入了波浪和水流对风机塔架及基础结构的影响。通过仿真验证系统的有效性,并探索最佳控制参数设置以提高整体性能和稳定性。本教程展示了如何在Simulink中实现一个海上风电场在复杂海况下的动态响应仿真模型,并分析其性能。通过这种方式,我们不仅可以验证所提出的技术手段在提高海上风电场稳定性和效率方面的有效性,还能进一步探索优化参数以适应特定的应用场景。

xiaoheshang_123的博客 167

VB摄像头监控

该VB模块可对摄像头捕获的画面进行录像等,可在创建的窗口进行控制

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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。

上一篇: ABAP编程学习:辨析ON INPUT、REQUEST、CHAIN-INPUT和CHAIN-REQUEST
下一篇: 迷失程序合集:LNnoip2020编程学习
普通网友
博客等级 码龄3年 17粉丝 0原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符  | 博主筛选后可见
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值