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
1.3k累计阅读
1.3k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
EF Core 动态拼接查询条件 (.NET)
在使用 Entity Framework Core (EF Core) 进行数据访问时，我们经常需要根据不同的条件动态构建查询。本文将详细介绍如何使用 EF Core 动态拼接查询条件，以实现更灵活和可扩展的查询功能。
在开始之前，确保你已经安装了适当版本的 EF Core，并且已经创建了一个数据库上下文（DbContext）和相关的实体类。
首先，我们需要定义一个方法，该方法接收一个查询表达式树（Expression Tree）和一个查询条件的输入参数，并返回一个 IQueryable 对象。这样我们可以在不执行查询的情况下构建查询表达式。
using System;
using System.Linq;
using System.LinqJW01二氧化碳传感器实战：5分钟搞定Arduino环境监测系统（附完整代码）
本文详细介绍了如何使用JW01二氧化碳传感器与Arduino快速搭建环境监测系统。通过简单的硬件连接和完整的代码示例，读者可以在5分钟内实现CO2浓度实时监测，并了解数据解析、常见问题解决及进阶应用扩展。JW01传感器的高精度和易用性使其成为创客和嵌入式开发者的理想选择。
新手也能懂的Simulink汽车二自由度建模：从公式推导到模型搭建全流程
本文为新手提供了在Simulink中搭建汽车二自由度模型的完整实战指南。文章从模型核心概念讲起，详细讲解了如何将车辆动力学方程转化为状态空间模型，并逐步指导在Simulink中实现从参数设置、模块搭建到仿真验证的全过程。通过阶跃转向测试和参数影响分析，帮助读者直观理解车辆转向特性，并提供了实用的模型扩展思路与常见问题排查技巧。
BDD测试之Behave使用入门
BDD测试（Behavior-Driven Development Testing）是一种基于“行为驱动开发”（BDD）理念。
面试真题集（十六）：TensorRT基础
TensorRT面试真题解析：从模型优化到推理部署 本文精选了14道TensorRT面试题，涵盖选择题、简答题和场景题，深度考察推理工程师对TensorRT核心技术的理解。内容聚焦三个关键维度： 基础概念 FP16推理中Softmax层为何需保持FP32（数值稳定性） INT8校准原理（EntropyCalibrator2的鲁棒性优势） Plugin V3接口的构建/运行分离设计 性能优化 Conv-BN-ReLU融合减少40-60%延迟的关键机制（访存优化） 动态Shape配置策略（min/opt/max
YOLO系列专栏（十二）：YOLO26行业落地实战(三)：无人机航拍场景——户外巡检（机载边缘计算+YOLO26）
无人机航拍巡检场景，面临机载设备算力/显存有限、高空航拍目标小、户外环境复杂（风力、光线变化）、图像抖动等问题，对模型的轻量化、实时性、小目标检测能力要求极高。YOLO26相比传统模型，在该场景中具备三大核心优势：✅ 极致轻量化：INT8量化+模型剪枝后，模型体积可压缩至5MB以内，显存占用控制在512MB以内，适配Jetson Xavier NX等机载边缘计算设备；✅ 小目标检测能力强：优化检测头与锚框配置，可精准识别高空航拍中的小尺寸目标（如线路断股、绝缘子裂纹），检测准确率高；
前端开发 - 完美解决video.js和flv.js视频同时播放数量限制的问题，即浏览器针对同一域名的请求限制问题（解决flv.js/video.js在页面播放最多不超过6个问题、浏览器并发请求限制数
flv.js，video.js，vue，reactive，uniapp，前端开发，flv视频同时播放有限制怎么办，浏览器限制多个视频同时播放怎么办，使用flv.js在前端播放,最多只能播放6个视频，突破浏览器域名并发限制，如何解决flvjs与videojs视频播放同时播放数量限制，video.js 一个页面同时播放多个视频的实例代码，解决网页视频最大只能播放6路的问题，浏览器只能播放6路http-flv流的解决方法，一个浏览器播放5个或者6个flv的视频就不能播放了，video.js 无法在页面同时播放多个
PPT制作能力在职场中真的重要吗？一定要美术功底才能学好PPT吗？
我推出PPT技能培训专栏啦！好的PPT可以让你事半功倍、2023拥有PPT能力还可以让你具备一门极有价值的斜杠能力。更多内容请进：梦幻训练营专栏。专栏内会用本人积累的近10年的PPT学习方法论在短时间内教会你如何制作出精美绝伦的PPT，每篇教程后还会免费赠送一些小福利，不可多得。
搭建自己的MQTT服务器，实现设备上云(Ubuntu+EMQX)
这篇文章教大家在ECS云服务器上部署EMQX，搭建自己私有的MQTT服务器，配置EMQX实现设备上云，设备数据转发，存储；服务器我采用的华为云的ECS服务器，系统选择Ubuntu系统。
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
RU DOS版、EFI版32bit/64bit BIOS常用工具
DOS版、EFI版的RU工具，可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
383
扫一扫
