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大家好 我是阿喵
今天教大家用如何用爬虫爬取可爱小姐姐的美照
第一步：现将python环境搭建好，工欲利其事必先利其器！
第二步：寻找目标网站，我选择的网站是http://www.win4000.com，里面有一个美女板块，里面有各种小姐姐的照片（你懂的）
第三步：分析目标网站的html源码及网页规则
通过分析我发现，该网页每个人物的每个图片都是
http://www.win4000.com/meinv1_2.html
http://www.win4000.com/meinv1_3.html
http://www.win4000.com/meinv2_1.html
http://www.win4000.com/meinv2_2.html
并且所有图片地址都存放在一个中
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爬虫爬取小姐姐图片
python 3 的爬虫 爬取小姐姐图片 批量下载 完整代码 提供参考 不懂可以私信我
PYTHON爬取COSPLAY小姐姐图片
原由地址https://www.xiaoweigod.com/code/1666.html 最近研究了一下PYTHON，都说很好用很强大 ，用一个爬虫程序试验 了一把，效果还不错，原教程地址 不过发现原著中所说的网页结构有所调整，所以也在原著基础上做了相应的调整，结果还成功了。不过好像没有什么 反反爬技术，继续多多学习，已经爬了近万张图片，有想要图片的可以留下邮箱。一会再换个网站试试。
Python爬取小姐姐源码
爬取目标网址：https://www.vmgirls.com代码简单明了内含注释 利用requests库进行爬取，图片以单页为文件夹存储图片
Halcon Socket TCP通讯：从算子调用到工业视觉系统集成
本文详细介绍了Halcon Socket TCP通讯在工业视觉系统中的应用与优化技巧。从基础算子调用到高并发处理，再到与PLC、机器人及MES系统的集成实战，提供了全面的解决方案和性能优化建议，帮助开发者高效实现工业视觉系统的通讯需求。
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ROS2机械臂实战：从基础控制到视觉抓取
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【工具使用】Keil常用的调试操作整理介绍
2、当选择了非0级优化时，调试可能会变得困难，具体表现在断点调试。该按钮是在进入了函数单步调试的时候，有时候你可能不必再执行该函数的剩余部分了，通过该按钮就直接一步执行完函数余下的部分，并跳出函数，回到函数被调用的置。：如果是在C语言窗口中，则是按单条语句执行，与单步调试不同的是，遇到函数不会进入函数内部，而是直接全速运行函数，并跳到下一条语句。但我的Keil软件中无法显示这个视图，并且通过修改系统试图，调试时，点击打开系统视图发生了闪退的现象，目前能想到的原因是：重新安装Keil，安装最新版本。
Ubuntu22.04中通过conda快速部署PyTorch开发环境（CPU/GPU双版本实战）
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AI智能体（AI Agent）作为人工智能领域的重要发展方向，正从概念走向工程实践。其核心原理在于赋予AI自主规划、执行和迭代的能力，使其不再局限于简单的问答，而是能够理解复杂目标、分解任务、调用工具并完成闭环。这一技术价值在于将AI从“对话伙伴”升级为“执行伙伴”，极大地拓展了自动化边界，在智能客服、数据分析、自动化运维等场景展现出巨大潜力。本文聚焦于如何利用opencode这一热门开发框架，快速构建具备实际能力的智能体。通过拆解其核心组件如技能库、规划器与运行时，并结合具体实践案例，系统阐述了从环境搭建
SpyGlass Lint脚本实战：如何用TCL自动化RTL代码检查（附完整配置模板）
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基于Python的军事装备数据爬取与分析：从网页抓取到数据可视化实战
本文将详细介绍如何使用Python最新技术栈构建一个完整的军事装备数据爬虫系统。我们将从爬虫基础讲起，逐步深入到异步爬取、反反爬策略、数据存储与分析等高级主题，最终实现一个能够自动抓取、清洗、存储和分析军事装备数据的完整解决方案。文章包含大量实战代码示例，涉及requests-html、aiohttp、Playwright等现代爬虫库，以及MongoDB存储、Pandas分析和Pyecharts可视化等技术。关键词：Python爬虫、军事数据、异步爬取、反反爬、数据可视化。
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
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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
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