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用户注册和邮箱验证是许多网站和应用程序中常见的功能之一。在 Django 中，我们可以轻松地实现用户注册和邮箱验证的功能。本文将介绍如何在 Django 中实现用户注册和邮箱验证，并提供相应的源代码示例。
首先，我们需要创建一个 Django 项目和一个应用程序。打开命令行界面，导航到您要创建项目的目录，并运行以下命令：
$ django-admin startproject myproject
$ cd myproject
$ python manage.py startapp accounts
这将创建一个名为 “myproject” 的 Django 项目和一个名为 “accounts” 的应用程序。
在项目的 settings.py 文件中，我们需要对邮件设置进行配置。找到以下设置，并进行相应的更改：
# settings.py
EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'
EMAIL_HOST = MPU6050 加速度计和陀螺仪传感器与 Arduino 的接口
虽然该模型并不能准确代表现实世界中加速度计传感器的构建方式，但它通常有助于理解为什么加速度计的输出信号通常以 ±g 为单位指定，或者为什么加速度计在静止时在 z 轴上的读数为 1g，或者在不同方向上您可以期望得到什么样的加速度计读数。该模块的核心是低功耗、低成本的 6 轴运动跟踪芯片 MPU6050，它将 3 轴陀螺仪、3 轴加速度计和数字运动处理器 (DMP) 集成到一个 4mm x 4mm 的微型芯片中。小时候，科学博览会上的陀螺仪总是让我们感到惊讶，因为它们以奇怪的方式移动，甚至似乎违背了重力。
对比实验系列：Efficientdet环境配置及训练个人数据集
可以通过下方链接下载Efficientdet源码。
Manimgl 进阶笔记
目录 一、调整视频输出的纵横比 二、调整预览窗口的尺寸 三、在开发工具中直接调试 一、调整视频输出的纵横比 调整输出视频的纵横比，在日常开发中属于比较基本的需求。要使用 Manimgl 得到期望的分辨率的视频输出需要修改以下几个地方： 第一步：在 custom_config.yml 配置文件中通过 camera_qualities设置视频的输出分辨率。manimlib 框架已经在 default_config.yml 文件中设置了四种视频输出分辨率，default_qualities 参数.
量化策略分享之ST股弱转强，回测年化123%
本策略的核心是精选ST股票，国九条过滤，防止直接暴雷退市，只做ST股票的弱转强，弱转强的认定标准是前日涨停但昨日不涨停，昨日收盘价大于前一日最低价，昨日收盘价大于10日线，昨日成交量大于前一日的成交量但小于前一日成交量的10倍，股价大于1；这类股票强势，有资金介入，能获得超额收益。
Python电梯II
输入数据有若干组（最多不超过1000组），每组数据的第一行为四个如题目中所示的整数N,u,d,s，由空格分隔开。第二行为不同楼层需要上下楼的请求, 最多不超过1000个。每个请求的格式为"from-to"，其中"from"和"to"为所在楼层和要到达的楼层，保证from!=to。
基石篇：多模态数据工程导论与核心概念
多模态数据工程不是写Python脚本处理几张图片，而是设计并构建一套系统，来规模化地生产高质量、可追溯、符合合规要求的模型就绪数据。本文将为你建立正确的数据工程思维框架，明确其在多模态AI生态中的战略地位，并为后续的深入学习奠定坚实基础。
关于1EDI3033AS驱动芯片误触发DESAT保护的问题
驱动参数匹配：检查门极电阻（Rg）是否合理，过小的Rg可能加剧dv/dt干扰，而过大的Rg会增加开关损耗。SiC驱动欠压故障分为上桥故障和下桥故障，利用上位机，配置软件后上桥故障使得TestC为1，下桥故障使得TestB为1。500V母线电压，10k载频，200hz电频率，假负载大电流闭环测试时，相电流高于60A之后报SiC驱动故障。600V母线电压，10k载频，200hz电频率，接水冷，假负载350A电流闭环测试。750V母线电压，10k载频，200hz电频率，接水冷，假负载350A电流闭环测试。
K8s（二十一）：在 Kubernetes 集群中部署 MySQL8.0 高可用集群（1主2从）
🔴 K8s（二十一）：在 Kubernetes 集群中部署 MySQL8.0 高可用集群（1主2从）
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
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