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首先，我们打开首页，输入关键词：女装。↓↓↓
点击找一下，后会跳转到商品页面，如下图所示↓↓↓
这个时候我们就可以看到女装商品分类，和一些推荐商品，
接下来我们不要急着爬这些商品数据，我们要找的是这些商品的分类目录地址。
谷歌浏览器右击检查页面，仔细观察会发现，每个分类的商品都有对应的地址，例如：连衣裙，对应的地址如下
我们进入连衣裙的href标签里面的地址，你会发现页面的标题已经从“女装”变成“女装-连衣裙”了，因为我们在这个页面看到的商品是经过淘宝分类后的，这一页内容只包含“女装-连衣裙”。
&nb
如何通过api来批量抓取1688的商品数据？（电商API_key免费获取）
买一样东西，淘宝上看一看，京东看一看，拼多多再看一看，总之呢，怎么便宜怎么来，小红书上，甚至有人测评，不同平台不同价格买的同一样商品，发货地址是一样的，从而来对比出哪一个平台的产品更具性价比。1688是阿里巴巴集团旗下的一款B2B电商批发采购平台，以批发和采购业务为核心，覆盖普通商品、服装、电子产品、原材料、工业部件、农产品和化工产品等行业，提供从原料采购、生产加工、现货批发等供应服务。请求参数：num_iid=610947572360。参数说明：num_iid:1688商品ID。请求示例（JAVA代码）
Python Selenium自动化采集阿里巴巴国际站数据并生成可视化Excel报告
Web数据采集（爬虫）是自动化获取互联网公开信息的关键技术，其核心原理是通过程序模拟浏览器行为或发送网络请求，从网页中提取结构化数据。这项技术的价值在于能够将海量、分散的网络信息高效、准确地转化为可分析的数据资产，从而支持市场分析、竞品研究、价格监控等商业决策。在电商、外贸、舆情监控等应用场景中，自动化数据采集能极大提升工作效率。本文以阿里巴巴国际站为例，详细阐述了如何利用Python的Selenium库应对动态渲染页面和反爬机制，实现从登录、翻页到数据提取的全流程自动化，并将采集到的商品价格、供应商信息等
[技术解析] 跨境电商如何实现供应链数字化？浅谈基于 Python 的 1688 数据采集与 RPA 清洗上架方案
在跨境电商（Cross-border E-commerce）的业务链条中，供应链数据的流转效率往往决定了企业的响应速度。对于许多铺货型（Dropshipping）或精铺型卖家而言，上游通常对接 1688 工厂，下游对接 Amazon、TikTok Shop 或独立站。“数据断层”。1688 提供的原始数据（非结构化 HTML）与跨境平台要求的标准数据（结构化 Listing）之间，存在巨大的差异。数据源杂乱：中文标题包含大量无效营销词，属性规格不统一。人工处理低效。
[硬核科技] 嫌跨境 ERP 太贵且笨？浅析如何用 Python+RPA 自建“1688 采集-清洗-上架”私有化中台，数据安全由自己掌控
在跨境电商（TikTok Shop, Temu, Amazon, Shopee）的规模化运营阶段，ERP 系统是标配。逻辑僵化：你想搞一套复杂的“重量分段定价公式”（例如：首重 50g 一个价，续重每 10g 一个价，还要加汇率波动系数），通用 ERP 的后台设置根本不支持这么细的逻辑。数据安全隐患：你辛苦挖掘的 1688 爆款链接，上传到云端 ERP，后台数据是否会被服务商“大数据分析”后卖给同行？这是行业内公开的秘密。“真正的护城河，是拥有一套完全受你控制的自动化系统。今天，我想从技术角度分享一套。
python 抓取1688店铺产品详情爬虫
python 抓取1688店铺产品详情，爬虫
Chapter 19 Digital Phase-Locked Loops
数字锁相环 Digital Phase-Locked Loops, 就是根据原来时钟信号, 产生新的时钟信号, which is locked or synchronized. (align 参考时钟边沿 or 在中间)
高分系列卫星轨道、传感器参数等信息
遥感卫星数据简介-高分系列
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本文深入探讨了电荷灵敏前置放大器的噪声优化策略，从理论分析到实践应用。文章系统解析了并联电流噪声与串联电压噪声的来源，并引入噪声等效原理与“冷电容”模型作为统一分析工具。通过详解a、b、c三类噪声的频谱特性，为实战优化提供了清晰指导。核心策略包括选用高跨导、低漏电流FET，优化静态工作点，采用共源-共基结构，精心设计反馈网络，以及实施严格的PCB布局、屏蔽与接地，旨在全面提升微弱信号检测系统的信噪比。
layui 监听单元格点击事件，获取当前点击单元格的内容以及 所在的表头内容
特别是后台的一些管理系统中 业务中还是会碰见这样的需求呢 如果你的后台使用layui写的话 在表格中 layui 也给我们提供了监听单元格的事件 但是好像不能直接获取到单元格内容个 和 对应的表头 其实 还是可以解决 换个角度使劲就会发现新的世界 不是吗 event: 'username' 在每个表头中加一个 event 属性 这样就可以监听表头事件 记住个 event 和 当前列的字段最好保持一样 table.render({ e...
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完成自主导航功能，并在小车移动至指定地点的过程中，通过机器人的摄像头检测导航途中所见物体，识别出物体的种类（框选出不同物体并标注识别结果），结合科大讯飞语音合成模块对识别结果进行语音播报。..............................
100天精通Python（爬虫篇）——第122天：基于selenium接管已启动的浏览器（反反爬策略）
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NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
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")
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
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