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
203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Android开发中，线程和并发处理是非常重要的概念。线程是一种执行单元，用于在应用程序中执行并发任务。并发处理是指同时进行多个任务的能力。在本文中，我们将深入探讨Android中的线程和并发处理，并提供相应的源代码示例。
一、线程基础
（1）继承Thread类：
public class MyThread extends Thread {
@Override
public void run(Android中的Handler与多线程应用实例
主要介绍了Android中的Handler与多线程应用实例,本文首先解释一下handler是用来干嘛的,然后通过例子介绍其在多线程中的应用,需要的朋友可以参考下
步进电机梯形加减速算法的实现
步进电机梯形加减速算法的实现
OpenNARS开源通用人工智能系统：非公理推理引擎实践指南
人工智能系统从基于规则推理到深度学习模型，始终围绕如何让机器具备智能决策能力。传统逻辑系统依赖公理和完备知识，而现实世界智能体需要在资源受限、信息不完备环境下进行推理。非公理推理系统（NARS）通过动态真值计算和信念修正机制，实现了在不确定性环境下的自主学习和决策。这种推理引擎的技术价值在于模拟人类思维的核心机制——归纳、演绎和学习，为通用人工智能（AGI）提供了不同于数据驱动的新路径。在应用场景上，NARS适用于游戏NPC决策、复杂系统故障诊断和自适应机器人控制等领域。OpenNARS作为NARS理论的开
【Cadence】PSS+PSTB/PAC仿真在开关电源稳定性分析中的关键应用
本文深入探讨了Cadence仿真工具中PSS+PSTB/PAC方法在开关电源稳定性分析中的关键应用。针对开关电源作为时变非线性系统的特性，传统STB仿真方法会失效。文章详细阐述了如何通过PSS分析找到周期稳态，并在此基础上利用PSTB或PAC进行精确的环路增益和小信号频响分析，为电源设计提供可靠的稳定性评估与补偿网络设计依据。
Nacos2.3.0与vastBase数据库的集成实践指南
本文详细介绍了将Nacos 2.3.0的默认数据库从MySQL迁移至vastBase数据库的完整实践指南。内容涵盖前期环境准备、源码改造、依赖配置、SQL适配、数据库初始化以及最终的编译打包与部署验证，旨在帮助开发者在国产化或特定技术栈场景下，实现微服务配置中心与vastBase数据库的自主可控集成。
做量化没有实时数据怎么行？我找到一个超级好用的Python库，速度还贼快！
今天咱们介绍了 pytdx 这个神奇的库，重点包括：✅ 安装 pytdx✅ 连接 TDX 服务器✅ 获取实时行情数据✅ 获取 K 线数据✅ 获取分笔成交数据总之，pytdx 是一个速度快、免费、易用的量化利器，适合 A 股量化交易的同学们使用。如果你还在为数据发愁，赶紧试试吧！🚀顺手再看就是对花姐最大的支持，期待你的关注。
PyQt5混搭qfluentwidgets实战：用FramelessWindow快速打造现代化界面
本文详细介绍了如何将PyQt5与qfluentwidgets组件库结合，利用FramelessWindow快速打造现代化无边框界面。通过环境配置、核心特性解析、组件混用技巧及高级样式定制，帮助开发者提升桌面应用的用户体验和美观度，特别适合需要现代UI设计的Python开发者。
C#上位机多PLC多协议开发思路：10年老工控师拆解实战方案，避坑90%的问题（附代码框架）
是否连接成功数据地址（如“DB1.DBW0”“D100”“4x0001”）
Midscene.js（2）：代码集成 + 桥接模式（Bridge Mode）的使用「喂饭教程」
本文将手把手教你从零开始搭建一个 Node.js 项目，集成 Midscene.js，并利用其桥接模式实现对 Chrome 浏览器的自动化操作。我们将通过具体的实例，来展示 Midscene.js 的便捷性与 AI 能力的结合。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
假人有限元模型
假人模型，车辆碰撞假人有限元模型，适合做乘员安全的研究者利用
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
1317
扫一扫
