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Python，一种强大的计算机编程语言，对于没有学过代码的同学，Python无疑是你的最佳选择，简单易学功能强大，使得它备受大家欢迎。有想学Python的朋友吗?
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Python工具教程
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Python基础教程
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Python高级教程-Python环境开发
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Python高级教程-数据分析
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2024年Python爬虫技巧-西瓜视频MP4地址获取_python 批量下载西瓜视频，Python开发面试2024
如果你也是看准了Python，想自学Python，在这里为大家准备了丰厚的免费大礼包，带大家一起学习，给大家剖析Python兼职、就业行情前景的这些事儿。
Python 爬虫实战——爬取视频（二）
上一篇讲了如何去获取标题和视频链接。这篇就跟大家讲一下如何去下载这些视频。本篇会以标题和视频链接为突破口，来寻找视频的下载链接藏在哪里。注意：分上下两篇来写，请先看Python 爬虫实战——爬取视频（一）。此程序是在Python 3.11.6 版本的环境下编写的，注意哦要不然程序可能运行不起来。运行完上面的程序，就会得到下面的东西。
Python实现视频下载
你可以根据自己的需求选择适合的库和方法来实现视频下载。这些库提供了很多其他的功能和选项，你可以查阅它们的官方文档来了解更多信息。在示例中，我们选择了第一个可用的视频流（即最高质量的视频），并使用。类接受一个视频的URL作为参数，并提供了一些方法来获取视频的相关信息，比如标题、时长等。，它是一个命令行程序，可以从多个视频网站下载视频。在Python中，我们可以使用一些库和工具来下载视频。，它是一个专门用于下载YouTube视频的库。替换为你要下载的视频的URL。替换为你要下载的视频的URL。
python视频教程（全38集）.zip
中谷Python视频教程。0基础学Python。
基于单片机控制的开关电源设计
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多尺度的连续小波变换分解后时间尺度图 墨西哥Marr小波函数： %delta 小波变换尺度 %N 小波函数的长度 %s 原始信号 %g 原始信号某个尺度下的小波变化系数 function g=Singularity_Detection(delta,N,s); %保持信号长度 n=length(s); % 构造墨西哥小帽小波函数 for index_x=1
ITK-snap医学图像标注方法（超级详细）——保姆级
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设计一个增益为20dB、带宽≥100kHz的同相放大器，电源电压±5V，要求输出摆幅≥8V。12位ADC的参考电压2.5V，输入信号1kHz正弦波，峰峰值2V。输入24V转5V/10A的同步降压电路，开关频率500kHz，计算MOSFET的损耗并选择型号（需提供TI官网截图）。设计无人机图传系统，要求1080P@30fps，传输距离2km。FPGA设计中，100MHz时钟域到50MHz时钟域传输32位数据，给出同步方案并画时序图。“设计一个输入12V、输出5V/3A的同步降压电路，给出关键参数计算”
Charles 抓包工具下载安装及基础使用
本篇内容主讲关于Charles抓包工具，从下载安装以及配置信息，证书的下载安装以及PC端设置代理配置和移动端设备配置代理，阐述关于Charles抓包工具面板的基础使用以及如何进行抓包和设置断点，对于软件测试以及对抓包感兴趣的读者可以翻阅！感谢大家的支持！！！
STM32H7多通道ADC数据采集实战：CubeMX配置ADC1/2/3+DMA/BDMA+Cache避坑全记录
本文详细解析了STM32H7多通道ADC数据采集的实战配置，涵盖CubeMX中ADC1/2/3与DMA/BDMA的协同设置及Cache一致性处理。通过硬件架构分析、DMA优化技巧和Cache避坑指南，帮助开发者实现高精度数据采集，适用于电机控制、环境监测等嵌入式系统。
ISP图像处理Verilog仿真框架搭建（附代码）
基于Verilog的图像处理仿真框架搭建
EEMD算法原理及应用
本博客主要概括EEMD算法的背景、原理以及应用，利用EEEMD对信号进行分解，然后选择有用的IMF分量进行叠加以完成信号的重构，从而实现对信号降噪。本文配套的源代码见文中链接，可供大家免费下载。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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！
华为的C语言编程规范.pdf
华为技术有限公司内部技术规范。本规范制定了编写C语言程序的基本原则、规则和建议。从代码的清晰、简洁、可测试、安全、程序效率、可移植各个方面对C语言编程作出了具体指导。
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