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嗨喽~大家好呀，这里是魔王呐 !
壁纸，有多种的类别和各种不同的风格，如：
风景、美女、唯美、动漫、花卉、节日等适合您的高清桌面壁纸
今天我们就来采集一下叭~
Python 3.8 解释器
Pycharm 编辑器
win + R 输入 cmd 点击确定, 输入安装命令 pip install 模块名 (pip install requests) 回车
在pycharm中点击Terminal(终端) 输入安装命令
1. 发送请求
模拟浏览器 对于url地址发送请求, 获取服务器返回响应数据
伪装 headers 请求头
2. 获取数据
3. 解析数据
提取我们想要的内容
4. 保存数据
import requests # 用来发送请求模块<工具>
import re # 提取数据工具
response = requests两层板如何做阻抗控制呢
两层板如何做阻抗控制呢？ 这个问题可能很多人都会碰到，有的人在此等需要做阻抗控制的情况都选择做多层板。诚然，多层板在阻抗控制，EMC等等方面都有着天然的优势。然而，在价格上和周期上，两层板就有着绝对优势了。 对于成本非常敏感而又不是太复杂的产品，尽管对于我们硬件工程师而言，有一堆的理由要求使用多层板。然而在面对毛都不懂的老板“别人能做，你为什么不能做？”这个问题时只得低下我们高傲的头颅。 好...
使用光流进行 Warp 的流程
光流描述了图像帧中每个像素的运动。前向光流：描述了帧中每个像素在帧中的移动位置。后向光流：描述了帧中每个像素在帧 中的移动位置。水平方向的位移（x 方向），即每个像素水平移动了多少像素。垂直方向的位移（y 方向），即每个像素垂直移动了多少像素。使用光流进行 Warp 的流程Warp 是通过光流对图像进行变换的操作，旨在将前一帧的像素根据光流场映射到下一帧的对应位置。Step 1: 计算光流我们首先需要通过光流网络（如 RAFT）来估算两帧之间的光流。我们可以获得前向光流（从帧 到帧。
教程 | 基于遥感生态指数(RSEI)模型的生态环境质量评价
使用数据：Landsat8 OLI数据，行列号：122036，时间：20210527。 使用软件：ENVI5.3、ArcGIS10.2 二、4个指数计算 下述的四个指数的计算均是通过ENVI软件中的Band math工具进行计算。 （1）绿度指数 在常用的植被指数当中，归一化植被指数 ( NDVI) 能够有效地反映植被的生长情况与植被 覆盖度等重要植被的物理性质，检测灵敏度高，能 够较为真实地展现区域的地表空间变化规律，已经得到广泛利用，本文采用 NDVI 指数来代表绿度指数。 在ENV
深入解析DisplayPort（DP）接口：从主链路到辅助通道的全方位技术剖析
本文深入解析DisplayPort（DP）接口的核心技术架构，重点剖析了其三大功能模块：承载视频/音频数据洪流的Main-Link主链路、负责链路训练与设备控制的AUX CH辅助通道，以及实现智能交互的HPD热插拔检测。文章详细阐述了主链路的可伸缩Lane配置、嵌入式时钟与数据成帧机制，以及辅助通道在设备发现、动态优化信号质量（链路训练）中的关键作用，揭示了DP接口实现高效率与灵活性的设计哲学。
宇树 Qmini 双足机器人完全开源：3D打印+5小时组装，新手也能玩转！
教育教学，成为学校里的竞赛竞技，实验课程，论文设计的本体；开发者可以根据开源资料轻松集齐所有零部件，结构件可全部通过3D打印完成（几乎完全不需要任何机加工零部件），搭配宇树的高可靠性电机和标准电池，客户打印完零部件后，大概仅需要3~5小时的手工组装即可得到一只可爱的Qmini小双足机器人本体。一起推动机器人的未来发展，创新没有边界，创造力是无限的，Qmini就是你的第一步，如果你也想做一个能动、能学、还能萌的小双足机器人，现在就可以动手了，全开源、真低门槛、只差你的3D打印机和一点点耐心。
QT如何使用CTK插件搭建应用程序框架？
CTK框架，QT框架，插件系统
利用恒源云云端GPU高效训练YOLOv11模型全流程：从环境搭建到模型部署，让你摆脱硬件束缚
记住，云端GPU的核心优势是“弹性算力+一键环境”，只要把握好这一工具特性，再结合YOLOv11的高性能模型架构，你也能在计算机视觉领域快速落地项目。接下来，我们从工具准备到模型部署，一步步带你实现高效训练，真正帮你解决资源与效率的核心痛点。凭借其在目标检测、实例分割任务上的卓越性能，成为众多开发者的首选。据统计，使用云端GPU训练YOLOv11，在处理百万级数据集时，训练效率较本地普通GPU提升。在安防监控中，YOLOv11可实时检测人员、车辆异常行为，某园区采用该方案后，异常事件识别准确率达。
使用 Simulink + Simscape Electrical 实现一个三相半控桥式整流器的仿真模型
本文详细介绍了使用Simulink搭建三相半控桥式整流器仿真模型的方法。主要内容包括系统结构设计、建模步骤（从创建项目到参数配置）、移相触发控制的实现，以及阻感负载下的动态响应分析。通过合理设置三相电源、整流器模块、触发控制和测量模块，实现了输出电压和电流波形的仿真观测。文章还提供了优化建议，如添加滤波器、实时控制等改进方向，为电力电子变换器的仿真研究和性能优化提供了实用指导。
IMSL CNL 7.0.0 (x86-64)Crack
IMSL CNL 7.0.0 (x86-64)Crack intel的算法库破解文件，这是C++版本的破解文件
锂电池充电器电路设计 里面有电路
锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
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")
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