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
100+累计阅读
140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
在过去的一年里，我投入了大量的时间和精力来学习编程。这篇文章将回顾我在这段时间里的学习经历，并分享一些我所学到的知识和经验。以下是我在不同阶段的学习内容和相关的源代码示例。
在开始学习编程的早期阶段，我主要专注于掌握编程的基础概念和语法。我选择了一门流行的编程语言，比如Python，作为我的起点。下面是一个简单的Python示例，用于打印“Hello, World!”：
print("Hello, World!")
通过学习基本的语法规则和常用的编程概念，我逐渐掌握了变量、条件语句、循环和函数等基础知识。这些知识为我后续的学习奠定了坚实的基础。
一旦我掌握了编程的基础知识，我开始进一步学习一些核心概念，如面向对象编程（OOP）和数据结构。这些概念对于构建复杂的应用程序非常重要。
下面是一个使用Python实现的简单类的示例，展示了面向对象编程的基本思想：
class 数字孪生实战：如何用Unity3D为工厂设备快速搭建可视化孪生体（附GitHub源码）
本文详细介绍了如何利用Unity3D游戏引擎为工厂设备快速构建可视化数字孪生体。通过实战案例，阐述了从3D模型导入、MQTT实时数据接入，到振动、温度等关键参数可视化呈现的完整流程。文章旨在为智能制造和工业物联网开发者提供一套灵活、低成本的技术方案，并附有完整的GitHub源码，助力实现设备状态的直观监控与预测性维护。
【脑电分析系列】第7篇：频域分析基石 — 功率谱密度（PSD）的计算与常见方法对比
本文探讨了脑电图（EEG）频域分析的核心方法——功率谱密度（PSD）。通过对比基于快速傅里叶变换的周期图法和更稳健的Welch's方法，指出后者通过分段、加窗和平均处理，能有效降低估计方差，更适合分析非稳态的脑电信号。文章详细解析了关键参数设置对频率分辨率和统计稳定性的影响，并提供了Python计算实现。PSD分析将复杂的脑电信号转换为直观的频域特征，为研究认知功能和神经疾病提供重要依据。
Orbbec-Gemini-335 深度相机 Python SDK 编译与配置全记录（Windows 环境）
本篇博客将围绕 Windows 系统下 Orbbec SDK 的 Python 配置与编译过程进行详细说明，主要使用的是官方提供的 pyorbbecsdk Python wrapper 项目以及 Orbbec SDK V2 Python Wrapper 配置指南，并记录了安装与配置过程中遇到的若干问题及解决办法。
Windows上python的spark环境搭建
1.安装好JDK 下载并安装好jdk-12.0.1_windows-x64_bin.exe，配置环境变量： 新建系统变量JAVA_HOME，值为Java安装路径 新建系统变量CLASSPATH，值为 .;%JAVA_HOME%\lib\dt.jar;%JAVA_HOME%\lib\tools.jar;（注意最前面的圆点） 配置系统变量PATH，添加 %JAVA_HOME%bin;%JAVA_HOME%jrebin 在CMD中输入：java或者java -version，不显示不是内部命令等，说明安装成功。
BVH2SMPL 实战笔记
BVH2SMPL 实战笔记
（五）激光线扫描-位移台标定
线激光属于主动测量方式，但是由于线激光的特性，我们只能通过提取激光中心线获取这一条线上的高度信息，那么要进行三维重建的话，就需要通过平移或者是旋转的方式，来让线激光扫描被测物体的完整轮廓，也就是整个表面。激光线的密度越高还原出来的物体越细腻，但由于数据量大，也会导致速度变慢。下面以位移台为例讲解，如何标定位移台。
YOLO11涨点优化：训练技巧 | 使用标签平滑（Label Smoothing）配合余弦退火学习率，防止过拟合，稳步提点
摘要： YOLO11凭借动态卷积核调度、自适应特征融合等创新成为2026年目标检测主流，但面临训练集过拟合问题。文章聚焦两种零推理开销的正则化技巧：标签平滑（Label Smoothing）和余弦退火学习率（Cosine Annealing LR）。标签平滑通过软化硬标签（如将[1,0]改为[0.9,0.1]）防止模型过度自信，代码中通过label_smoothing=0.1一键启用；余弦退火则以平滑的余弦曲线调整学习率（cos_lr=True），避免阶梯衰减的震荡。两者组合可显著缩小YOLO11训练与验证
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” ✨
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
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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。
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0， 兼容Win7/Win10/WinXP，解压就可用。
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
477
扫一扫
