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Dracula是一款Dark（Deep）系的配色方案。点我下载
下载完成，会获得一个.vssettings文件。
照着Dracula官网的安装步骤，就可以顺利安装完成啦。
NOTE ：
这里还有一个很多人推荐过的网站，https://studiostyl.es/，里面有很多VS的配色，也支持在线编辑~
Dracula的配色方案很棒，但是有一点不顺心，就是函数名与变量名的颜色是一模一样的。
当很多变量与函数在一起的时候，不太容易分清楚。
而VS Code上的Dracula配色方案却没有这个问题，那就改进一下VS的配色吧。
这是一个XML文件，可以用编辑器打开。
添加一行代码，如下图所示。method name的含义就是字面意思（等于没说~），其他项同理。Foreground为字体颜色，我们修改这个就行了。
下载地址 ： https://gi
C++终端开发环境搭建：从编译器到调试器的完整工具链指南
在软件开发领域，构建系统与开发工具链是提升工程效率的核心。CMake作为现代C++项目的标准构建系统，通过声明式配置管理多平台编译，解决了传统Makefile在项目规模扩大时的维护难题。其技术价值在于实现跨平台构建的标准化，并能与持续集成流程无缝对接。结合语言服务器协议（LSP）如clangd，开发者能在终端编辑器中获得智能代码补全和实时错误检查，大幅提升编码体验。这些工具在服务器部署、远程开发和自动化脚本等场景中尤为重要，尤其适合需要轻量、高效、可控开发环境的C++项目。本文以CMake和LSP为核心，详
Visual Studio2008配色方案
Visual Studio2008配色方案
Visual Studio 2008 配色方案
自己亲手做的VS2008配色方案，非常漂亮，配合小苹果使用
visual studio 配色方案
几款很喜欢的vs配色方案，写代码的时候，不用被刺眼的白色屏幕伤害眼睛。希望大家喜欢。
Unity Hub报错The Hub as encountered a critical error and must close
如果注册表HKEY_LOCAL_MACHINE\SOFTWARE\Unity Technologies\这个目录下有Hub文件，直接删掉然后重装就可以。C:\Users\Administrator\AppData\Roaming\Unity 作者：SevenVee。HKEY_CURRENT_USER\Software\Unity 下所有项，尝试过，卸载重装，清除临时文件无果后，最终决定删除所有与Hub有关的文件。重装unity后可以打开了。结果：卸载重装后还是会出现此问题。结果：重装后还是无法解决。
别再只调API了！深度拆解Dify文件上传的两种传参方式（sys.file vs. inputs.upload_file）
本文深度解析Dify平台文件上传的两种传参方式——sys.file与inputs.upload_file，详细对比其设计原理、技术实现及适用场景。通过代码示例和性能分析，帮助开发者在本地大模型应用中做出精准技术选型，提升文件处理效率与系统稳定性。
告别TwinCAT：手把手教你用IgH EtherCAT Master在LinuxCNC上搭建实时运动控制平台
本文详细介绍了如何利用IgH EtherCAT Master和LinuxCNC搭建开源实时运动控制平台，替代商业软件TwinCAT。通过分步指南，包括实时Linux环境配置、EtherCAT主站安装、LinuxCNC集成及多轴同步控制实现，帮助开发者构建高性能、低成本的工业自动化解决方案。
后仿真中《SDF反标必懂连载篇》之 Negative Delay （理论篇）
今天，学习一下SDF文件中的负数延时问题。比如下面的图片中的SETUPHOLD 中的hold time 为负数。
嵌套学习 (Nested Learning, NL)：赋予AI持续学习能力的革命性范式
嵌套学习（Nested Learning, NL）的核心哲学在于彻底颠覆传统深度学习中将模型架构（Architecture）与优化算法（Optimization）视为两个独立组件的“扁平化”视角 。传统方法通常将神经网络的结构设计和其训练过程分离开来，模型架构是预先定义好的静态蓝图，而优化算法（如SGD、Adam）则是一个外部的、统一的规则引擎，负责在训练期间调整模型参数 。这种分离的视角隐藏了模型内部不同组件之间复杂的梯度流和动态更新过程，将整个学习系统视为一个单一层次的优化问题。然而，嵌套学习范式提出，
cherry使用MCP协议Streamable HTTP实践
摘要：Streamable HTTP协议是针对传统HTTP+SSE传输方式的改进方案，解决了连接恢复、服务器资源占用和单向通信等问题。该协议基于普通HTTP实现，支持无状态服务器、纯HTTP服务和多轮对话，同时兼容现有基础设施。通过演示展示了服务端和客户端的配置方法，体现了其灵活性和实用性。MCP（多轮对话协议）作为扩展LLM能力的关键技术，代表着未来交互式AI的发展趋势。
Oracle EBS R12.2.X JSP部署、编译和配置相关操作。
系统环境 RDBMS : 12.1.0.2.0 Oracle Applications : 12.2.6 背景 在某项目上，需要开发JSP相关程序，比如单点登陆、打印二维条码。当前把JSP部署编译以后，访问提示错误：Requested resource or page is not allowed in this site， 通过查SR发现Oracle EBS R12.2.X版本对客制化JSP进行权限控限，需要设置某些配置文件。 实例操作步骤 上传JSP文件 通常上传到$OA_HTML目录或其子目
优快云独家首发！万字长文，YOLOv5/v7/v8算法模型yaml文件史上最详细解析与教程！小白也能看懂！掌握了这个就掌握了魔改YOLO的核心！
🍀🍀🍀YOLOv5/v7/v8算法模型yaml文件史上最详细解析与教程，全网最强，学不会算我输！！！
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")
Data-Structures-and-Algorithm-Analysis-in-Java.pdf
作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...
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。
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
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