墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
160平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
在本文中，我将向您详细介绍如何在Fedora Linux操作系统中配置Apache、SVN和Trac服务器。通过按照以下步骤进行操作，您将能够搭建一个完整的版本控制和项目管理环境。
sudo dnf install httpd
sudo systemctl start httpd
sudo systemctl enable httpd
sudo dnf install subversion mod_dav_svn
sudo svnadmin create /path/to/repository
请将/path/to/repository替换为您
手把手教你用微信小程序实现智能硬件音视频通话（含Linux/RTOS适配）
本文详细介绍了如何利用微信小程序硬件框架（WMPF）和音视频SDK，在智能硬件（包括Linux与RTOS设备）上实现音视频通话功能。文章从架构设计、设备端SDK集成、小程序VOIP插件开发到跨平台调试与性能优化，提供了全链路实战指南，帮助开发者打通微信小程序与嵌入式设备间的实时音视频通信。
GAN实战：用Python和TensorFlow打造你的第一个生成对抗网络（附完整代码）
本文是一份详细的GAN实战指南，指导读者使用Python和TensorFlow框架从零开始构建并训练一个生成对抗网络。文章涵盖了环境搭建、MNIST数据预处理、生成器与判别器模型构建、对抗性损失函数定义、训练循环编写以及关键的调试技巧，旨在帮助开发者通过动手实践深入理解GAN的核心原理与实现过程。
基于多物理参数数据融合和先进人工智能算法的锂电池热失控监测传感器
基于多物理参数数据融合和先进人工智能算法的锂电池热失控监测传感器是多种方案中的优选项！是一种快速、准确、可靠、应用广泛的传感方案！可有效监测锂离子电池热失控风险，保障化学能储能电站安全！
【OpenClaw企业级智能体实战】第09篇：OpenClaw金融投研实战：17个场景+完整代码，打造专属数字研究员
金融投研面临信息过载、流程重复、数据安全合规三大核心痛点，OpenClaw作为本地部署的新一代AI智能体，凭借“长期记忆+主动执行+数据不出域”的优势，成为券商分析师集体追捧的效率工具。本文基于8家头部券商公开研报，拆解OpenClaw在金融投研的17个核心应用场景（信息处理、研报撰写、量化策略、流程自动化），详解原理、技术选型、实操流程，并提供可直接运行的完整代码与执行结果。文章兼顾新手入门与进阶提升，强调安全隔离部署与幻觉防范，读者可按步骤搭建专属投研智能体，将信息处理、数据整理等重复工作效率提升80%
HyperLynx（二十二）DDR（五）DDRx总线时序模型设计
DDR3 的数据选通信号与时钟信号的频率是一样的，在数据选通信号写操作时，控制器典型的输出方式是数据选通信号边沿与时钟近似对齐。该界面中显示了所有需要编辑的参数，包括地址/命令信号的时序、控制信号的时序、读和写操作的数据选通和数据信号时序、写操作的数据选通和数据掩码信号时序，以及建立和保持时间的降额参数。由于DDRx总线对时序的要求非常严格，随着速率的提升，时序的要求会更高。反之也是一样的，如果数据选通信号的上升沿在时钟信号上升沿的后面到达 DRAM，那么数据选通信号的下降沿就会在时钟信号的前面到达。
STM32CubeMX之DAC产成正弦波
“在之前的文章中曾经介绍过用TIM产生PWM做DAC用，STM32定时器输出PWM做DAC。部分型号的STM32单片机内部有专用的DAC，本篇文章将介绍如何用内部DAC+DMA产生正弦波...
CAPL与人工智能结合
AI模型是指通过机器学习算法训练得到的模型，可以用于分类、回归、聚类等任务。常见的AI模型包括神经网络、支持向量机、决策树等。在汽车电子系统中，AI模型可以用于故障诊断、驾驶行为分析、自动驾驶等场景。实时决策是指在汽车电子系统中，根据AI模型的预测结果，实时做出决策。实时决策可以用于自动驾驶、故障诊断等场景。通过本文的学习，读者应该对CAPL与人工智能结合有了初步的了解，并能够编写复杂的CAPL脚本。
YOLOv8改进---BiFPN特征融合
yolov8结合BiFPN进行改进
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。
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
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
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
331
扫一扫
