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
200+累计阅读
217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
近日，有关OPPO公司的两则消息引起了广泛关注。首先是关于OPPO给离职员工补发年终奖金的消息，这是对之前克扣的弥补；其次是亿条领英用户数据被出售的消息，这引发了人们对数据安全问题的担忧。此外，还有关于HarmonyOS操作系统的报道，该操作系统应运而生，成为了人们对Android和iOS的新选择。
OPPO公司近期在公开信中宣布，他们将补发年终奖金给之前离职的员工，以弥补之前的克扣行为。这一决定对于改善OPPO的声誉来说至关重要。作为一家知名的科技公司，OPPO应该以公平和公正的方式对待员工，维护良好的劳动关系。这次补发年终奖的举动，向外界传递了一个积极的信号，显示了OPPO对员工福利的重视。
以下是示例代码，展示了一个简单的员工离职和年终奖发放的情景：
class Employee:
def __init__(self, name, resignation_date, 基于Android13的系统启动流程分析（三）之FirstStageMain阶段
init进程是Android系统中用户空间的第一个进程，作为第一个进程，它被赋予了很多极其重要的工作职责，比如创建zygote(孵化器)和属性服务等。init进程是由多个源文件共同组成的，这些文件位于源码目录system/core/init1. Bootloader 引导当按下设备电源键时，最先运行的就是 bootloader（固化在ROM的程序），bootloader 的主要作用就是硬件设备（如 CPU、flash、内存）的初始化并加载到RAM，通过建立内存空间映射，为装载 Linux 内核做好准备,。
W5500以太网模块官方驱动移植
W5500驱动移植
工业相机图像格式深度解析：从Mono8到Mono12的选型与应用
本文深度解析工业相机黑白图像格式，重点对比Mono8、Mono10与Mono12的核心差异。文章从位深、动态范围和数据量等关键概念切入，详细阐述了不同格式的优缺点及适用场景，为工业视觉项目中的相机选型提供实战指南，帮助用户在精度、速度与成本间做出最优决策。
你以为你懂 Java 反射？看了这篇源码分析，我发现我以前都错了
摘要：本文讲述了一次生产环境Metaspace内存溢出的故障排查经历。事故源于一个通用数据导出功能中频繁使用反射调用method.invoke()，触发了JVM的"膨胀机制"：当同一Method对象调用超过15次时，JVM会动态生成GeneratedMethodAccessor类，导致Metaspace被撑爆。文章分析了问题根源在于循环中反复调用getMethods()创建新Method对象，并提出了三种解决方案：缓存Method对象、改用MapStruct/MethodHandle工具
宇树 Qmini 双足机器人完全开源：3D打印+5小时组装，新手也能玩转！
教育教学，成为学校里的竞赛竞技，实验课程，论文设计的本体；开发者可以根据开源资料轻松集齐所有零部件，结构件可全部通过3D打印完成（几乎完全不需要任何机加工零部件），搭配宇树的高可靠性电机和标准电池，客户打印完零部件后，大概仅需要3~5小时的手工组装即可得到一只可爱的Qmini小双足机器人本体。一起推动机器人的未来发展，创新没有边界，创造力是无限的，Qmini就是你的第一步，如果你也想做一个能动、能学、还能萌的小双足机器人，现在就可以动手了，全开源、真低门槛、只差你的3D打印机和一点点耐心。
Deepfake检测模型-PyDeepFakeDet项目复现实验报告-深度学习
该实验是对于https://github.com/wangjk666/PyDeepFakeDet模型的复现。PyDeepFakeDet 是一款集成的、可扩展的 Deepfake 检测工具。目标是提供最先进的 Deepfake 检测模型以及用于在常用 Deepfake 数据集上训练和评估新模型的接口。
openpi 入门教程（一）安装、训练、微调与推理
OpenPI是由物理智能团队发布的机器人开源模型和软件包，包含两种主要模型：基于流的扩散视觉-语言-动作模型（VLA）的π₀模型和基于FAST动作标记器的自回归VLA的π₀-FAST模型。这些模型已在超过10,000小时的机器人数据上进行了预训练，并提供了基础模型检查点，支持开箱即用或根据用户数据集进行微调。OpenPI的实验性质意味着它可能不适用于所有平台，但鼓励研究人员和从业者进行创造性实验。运行这些模型需要至少具备NVIDIA GPU，推荐使用RTX 4090或更高规格的显卡。OpenPI支持在Ubu
美团mtgsig 1.2算法分析
mtgsig1.2的一些算法总结
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
锂电池充电器电路设计 里面有电路
锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。
数学建模模型总结
这是本人在两年内准备参加数学建模竞赛搜集到的模型资料，已经经过了本人的筛选和整理，希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助！
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
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
5496
扫一扫
