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首先申明，该文章只可以用于交流学习，不可以用于其他用途，否则后果自负。
很多人学习python，不知道从何学起。
很多人学习python，掌握了基本语法过后，不知道在哪里寻找案例上手。
很多已经做案例的人，却不知道如何去学习更加高深的知识。
那么针对这三类人，我给大家提供一个好的学习平台，免费领取视频教程，电子书籍，以及课程的源代码！
QQ群：961562169
现在国家对网络安全的管理，越来越严，但是还是有一些不法网站逍遥法外，受限于国内的人力、物力，无法对这些网站进行取缔。
今天演示的这个网站，就是属于非法的网站。
首先看登陆界面。
抓取登陆信息。
使用Post Form表单的形式，进行用户名和密码的提交。接下来我们看提交的用户名和密码。
我输入的密码和用户名一致，说明该站对提交的密码进行了处理，等一会我来看，密码是怎么处理的，现在我们看服务器的返回信息。
服务器返
编写脚本获取 token 对网站后台密码破解
每次访问页面这个 user_token 都会变化导致不能重复提交。验证原理每次页面生成 user_token 存在 seesion 里面每次登录用 session 里取出来然后验证如果验证成功 就进行密码匹配。如果验证不成功就输出 csrf 错误。
使用python假装黑客，破解朋友的网站密码
如何破解iphone登陆密码 好好地为什么说这个？ 周末看了一篇关于如何破解iphone手机密码的文章，瞬间觉得科学技术不是第一生产力，why？ 剧“可靠消息”称，即便美国FBI也无法轻易的对iphone手机进行暴力破解，当然美国有一家黑客公司科针对iphone进行破解，单收费过万美金。 那么铺天盖地的iphone手机密码破解“黑客”是怎么做的？ 无非是骗，他们假装成各类官方人员告诉你发现你的ip...
【趣味案例】用Python假装黑客装逼，当着朋友的面破解他的网站密码！
如何破解iphone登陆密码 今天看了一篇关于如何破解iphone手机密码的文章，瞬间觉得科学技术不是第一生产力，why？ 根据“可靠消息”称，即便美国FBI也无法轻易的对iphone手机进行暴力破解，当然美国有一家黑客公司可针对iphone进行破解，单收费过万美金。 那么铺天盖地的iphone手机密码破解“黑客”是怎么做的？ 无非是骗，他们假装成各类官方人员告诉你发现你的iphone手机存在...
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用HAL库复现江协科技，让嵌入式学习之路更顺畅。
使用NXP SBC插件开发FS26软件
FS26作为功能安全可达ASIL D的SBC，在配合NXP MCU的使用中非常广泛，本文介绍基于NXP官网的SBC代码包快速进行FS26软件的开发FS26使用过程中还有不少坑的地方，后面有空可以再介绍相关的功能。
全面解析量化价值投资多因子模型的原理
明明知道“买便宜的好公司”是价值投资的核心，但面对3000+只A股，如何快速筛选出“便宜又好”的标的？主观判断总被情绪干扰——明明PE很低的股票，买了却不涨；或者因为“感觉”错过牛股？想规模化投资，但手动分析100只股票就要花一周，根本无法覆盖全市场？量化价值投资的多因子模型，正是解决这些问题的钥匙。它将价值投资的核心逻辑（“便宜”“好公司”）转化为可量化的因子，用数学模型替代主观判断，实现系统化筛选、可回测验证、规模化复制的投资决策。为什么多因子模型能有效预测股票收益？
适航规章、标准，书籍资料下载汇总（增加DO-160G中英文版）
很多读者之前问我有没有DO-160G的资源，现在终于找到啦！如果大家有其他想要的资源，可以在留言区告诉我，有空我会帮大家找找看～文末有下载链接：更新日志：2024-9-28：初版。2024-10-21：增加DO-330英文版PDF下载。2024-10-30：增加DO-160G中文版（非机翻）和英文版PDF下载。以下是一些收集到的适航相关的资料，分享给大家。（如果没有你想要的资料，请留言，等找到会及时更新！
09 SAP CPI 访问公共API接口
我在网上找了一个公共API，入参输入汉字返回拼音。至此，一个真正的访问API的接口就做好了。点击保存时可看到红叉报错。
为什么 CL_DEMO_OUTPUT 不能用于生产用途：不仅是 demo 标签那么简单
摘要：CL_DEMO_OUTPUT 虽能快速展示ABAP数据，但存在严重生产风险：1) 官方定位为示例工具，不承诺兼容性与支持；2) 非Released API，云环境直接不可用；3) 依赖SAP GUI，缺乏日志关键特性；4) 存在版本漂移、类型限制、性能隐患及安全缺口。典型事故案例包括后台Job崩溃、Gateway链路污染和云迁移失败。生产环境应改用结构化日志(如SLG1)或稳定API输出，避免将调试工具误作工程方案。（149字）
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
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