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
2.3k累计阅读
2.3k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
将申请的AccessKeyId,AccessKeySecret切记保存好
授权以上四个权限,若是主账号不需授权
API文档地址: API文档地址https://help.aliyun.com/product/29697.html?spm=a2c4g.11186623.6.540.2a2a1cebspZpSW
sdk: pip install aliyun-python-sdk-alidns
#!/usr/bin/env python
#coding=utf-8
import json
from aliyunsdkcore.client import AcsClient
from aliyunsdkalidns.request.v20150109.AddDomainRecordRequest import AddDomainRecordRequest
from aliyunsdkalidns.树莓派+移动IPv6：用ddns-go实现零成本外网访问家庭NAS（阿里云DNS版）
本文详细介绍了如何利用树莓派、移动宽带IPv6和ddns-go工具，结合阿里云DNS服务，实现零成本外网访问家庭NAS的完整方案。文章涵盖了从环境准备、ddns-go部署、阿里云API配置，到IPv6防火墙设置、Nginx反向代理及安全加固的全过程，为家庭用户提供了一套稳定、安全且高效的内网穿透解决方案。
【odoo】群晖NAS使用Docker配置阿里云国际域名DDNS实现外网访问
一、设置域名解析 登录阿里云控制台域名服务，设置域名解析，添加一个A记录到任意IP 例如：设置 A 记录 nas.youdomain.com 到 8.8.8.8 二、创建AccessKey 在阿里云控制台右上角头像下拉框找到 AccessKey 管理 创建 AccessKey，建议使用子用户创建 授予子用户管理DNS的权限，只需要添加这一项权限即可，请及时复制保存 AccessKey 的信息 三、Docker镜像下载 打开Docker的注册表，搜索aliyun。下载 chenhw2/aliyun-d
阿里云动态域名解析实战：从零搭建家庭服务器外网访问方案
本文详细介绍了如何利用阿里云动态域名解析（DDNS）服务，从零搭建家庭服务器的外网访问方案。内容涵盖域名购买与配置、AccessKey安全创建、核心Java代码实战（包括多路并发获取公网IP与调用阿里云API），以及通过系统定时任务实现自动化部署与优化，解决家庭宽带IP不固定的访问难题。
SpyGlass CDC实战：如何用sgdc约束文件解决跨时钟域报错（附常见错误排查）
本文详细介绍了如何使用SpyGlass CDC工具编写sgdc约束文件解决跨时钟域(CDC)报错问题。从基础语法到实战案例，涵盖时钟声明、复位约束、路径豁免等关键技巧，特别针对SPI断续时钟等特殊场景提供解决方案，并附常见错误排查指南，帮助工程师高效完成CDC验证。
Fluent新手避坑：遇到‘Floating point exception’别慌，这7个排查步骤帮你搞定
本文为Fluent新手提供了遇到‘Floating point exception’错误时的7个详细排查步骤。从理解浮点异常的本质到边界条件检查、时间步长设置、求解器选择、网格优化、亚松弛因子调节以及高阶格式的取舍，帮助用户快速定位并解决问题，确保CFD仿真的稳定性和准确性。
从12摄像头到纯视觉：小鹏AI鹰眼方案如何用LOFIC技术省掉激光雷达？
本文深入解析小鹏汽车AI鹰眼方案如何通过LOFIC技术实现纯视觉自动驾驶，替代传统激光雷达。LOFIC摄像头在动态范围、噪声控制和HDR优化方面表现卓越，结合800万像素双目广角摄像头，达到激光雷达级测距精度。该方案不仅降低成本，还提升系统可靠性和用户体验，展示了纯视觉路线的技术潜力与边界。
【原理图PCB专题】Cadence17.4 PCB位号重排与反标
Cadence17.4 PCB位号重排与反标比Cadence16版本更简单一些，那到底如何操作？
Python 爬虫实战：爬取哔哩哔哩知识区，抓取高赞课程信息
哔哩哔哩（Bilibili）是一个广受欢迎的综合性视频社区，其中的知识区汇聚了大量优质的教育、科普、技术等课程内容。本文将指导读者如何使用 Python 爬虫技术从哔哩哔哩知识区抓取高赞课程信息，并进行数据分析。本文深入探究了利用 Python 爬虫技术从哔哩哔哩知识区抓取高赞课程信息的全流程。从前期的环境搭建、模拟登录，到核心的课程数据抓取、存储，再到后续的数据分析与可视化，都详细展开讲解并提供代码支持。通过此方法所获取的数据，能够助力创作者优化内容、用户发现优质课程、平台洞察用户需求。
银河麒麟操作系统最佳实践专栏介绍
本专栏聚焦国产银河麒麟操作系统（桌面版/服务器版）的实战应用，提供系统安装、配置优化、应用部署、故障排查等实用指南。内容基于真实操作场景，涵盖Linux通用技术（如RAID配置、环境变量管理）、服务器系统专项（如数据库迁移、驱动安装、安全加固）及桌面系统技巧（如PXE安装、镜像封装）。面向IT运维、开发者和国产化转型人员，突出实战性和差异化，帮助用户规避常见问题，提升系统管理效率。所有教程均配有详细步骤和截图，确保可操作性，并持续更新适配新版系统。
一种白平衡增益计算中白点权重计算简易实现方式
一种白平衡白点距离权重简易实现方式
大疆RoboMaster嵌入式面试，电机FOC调不出来当场走人
摘要：大疆RoboMaster嵌入式工程师面试以实战能力著称，重点考察FreeRTOS任务调度、FOC电机控制和CAN总线三大核心技能。FreeRTOS部分涉及优先级调度、栈溢出排查及信号量与互斥锁的区别；FOC控制要求现场编写电流环代码，并解释Clarke/Park变换原理；CAN总线则关注仲裁机制、CANopen协议应用及错误帧排查方法。面试强调工程实践，需具备STM32开发经验，建议搭建实际环境进行练习。该岗位被誉为"硬件版修罗场"，充分体现大疆对实际开发能力的严格要求。
pscad IEEE标准系统搭建说明
pscad IEEE标准系统搭建说明，包含典型的IEEE 9节点、14节点、30节点、39节点和118节点系统。内含标准测试系统的原始数据和潮流结果。
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")
华为的C语言编程规范.pdf
华为技术有限公司内部技术规范。本规范制定了编写C语言程序的基本原则、规则和建议。从代码的清晰、简洁、可测试、安全、程序效率、可移植各个方面对C语言编程作出了具体指导。
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。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
838
扫一扫
