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
186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
Python实现：循环链表中任意位置的拆分与合并
循环链表是一种特殊的链表，其尾节点指向头节点，形成一个环状结构。在实际应用中，我们可能需要对循环链表进行拆分或合并操作。本文将介绍如何使用Python来实现循环链表的拆分与合并操作，并提供相应的源代码。
首先，我们定义一个Node类来表示链表节点，包含一个value属性和一个next属性，next属性指向下一个节点。
class Node:
def __init__(self, value):
self.value = value
self.python：实现拆分循环链表(附完整源码)
python：实现拆分循环链表(附完整源码)
STC89C52RC单片机原理图详解
左侧A0和右侧B0为一 一对应的关系，左上角DIR为控制数据方向端口，如果DIR接高电平是将左侧数据A0~A8送到右侧B0~B8，如果DIR接低电平，是将右侧数据B0~B8送到左侧A0~A8，由于在电路板上LE始终与VCC连接，因此这里。74HC595是串行输入，并行输出的移位寄存器，可用3根线输入串行数据，8根线输出并行数据，多片级联后，可输出16位、24位、32位等，常用于I/O口扩展。由于数码管为共阴极，给数码管下面接口输入阳码时，LED才亮，但是直接将P0~P7的高电平输入到数码管时，
【零基础教程】HEC-RAS 1D/2D全流程模拟指南｜从水动力到水质建模一次搞定！
HEC-RAS由美国陆军工程兵团水文研究中心研制，具有完整的一二维耦合的水动力及水环境模拟功能，也具有闸、涵洞、桥梁以及泵站等水利构筑物的模拟能力；同时也可以与HEC研制的水文模型（HMS）、洪水淹没风险和损失分析软件（FDA）等耦合，是免费软件中使用最方便、应用最广泛的软件之一。2.HEC-RAS与HEC-HMS的耦合（不详细讲HEC-HMS）3.淹没风险分析软件HEC-FDA与HEC-RAS耦合。案例十八：HEC-RAS与HEC-HMS的耦合。案例十九：HEC-RAS与HEC-FDA的耦合。
【20230415】Ubuntu22.04双屏显示触屏设置，解决触摸点和响应位置不一致的问题
双屏显示时，如果存在一个触摸屏，有可能会出现触摸点和响应位置不一致的问题。我的显示器配置如下：一个34寸，3440＊1440；一个22英寸触摸屏，1920＊1080。
RWEQ土壤风蚀模型的估算与变化归因分析
本次以介绍RWEQ模型的基本原理及数据需求，再结合案例讲解RWEQ模型中区域地理空间数据库构建的方法和各个参量提取的原理与方法，最后结合典型案例进行综合讲解风蚀模数估算、制图及归因分析。空间分析与制图：提取分析、局部分析、邻域分析、区域分析、插值分析、平滑处理、像元提取、采样与重采样。以某实际应用为案例，掌握RWEQ模型进行当地土壤风蚀量空间分布分析整体流程。地图服务器、在线地图、范围指示器、格网、表格、基础地图服务使用规则介绍。掌握土壤风蚀模型的原理、优势点及因子组成。
AWS入门实践-如何在AWS云上创建一个内外网隔离的生产环境
在 AWS 上建立一个内外网分离的生产环境,可以减少应用服务的暴露面，有效的保证你的应用服务器的安全。通常我们会将web应用放在外网的子网内，数据库服务器等放在内网的子网。我们将按照下图来部署动手实践环境，实现在public subnet的EC2虚拟机可以直接通过互联网可以访问，在Private subnet的EC2虚拟机不能通过互联网直接访问，但是却可以在内部来访问互联网安装应用等等。
STM32G474实战：数字LLC电源控制程序从零到跑通（附完整代码解析）
本文详细介绍了基于STM32G474微控制器开发数字LLC电源控制程序的完整实战流程。从硬件平台搭建、CubeMX工程配置，到核心控制逻辑、双速率中断框架、数字PID实现，再到Burst模式、状态机等高级策略与异常处理，提供了从零到跑通的系统化指南与代码解析，助力开发者掌握高效数字电源的程序编写与调试技巧。
SwinTransformer改进（5）：与CBAM结合的视觉模型
这个模型的核心思想是将Swin Transformer的强大特征提取能力与CBAM的注意力机制相结合，以提升模型对重要特征的关注度。CBAM模块：包含通道注意力和空间注意力两个子模块Swin Transformer主干网络：作为基础特征提取器集成结构：在Swin Transformer的不同阶段插入CBAM模块使用移动窗口(Moving Window)机制减少计算量层次化设计，特征图逐渐缩小，通道数逐渐增加在不同阶段使用Patch Merging进行下采样。
数字游民产业小报告 · 2025
2025 年全球数字游民约（不同统计口径差异很大，低估法约 4,000 万，高估法 8,000 万），美国来源占比最高。：2025 Q1 约岗位允许一定程度远程；2024 年美国员工至少部分远程（全球 2023 年约 28%）。：到 2025 年，提供数字游民/远程工作签证（媒体口径 50–73 个不等；福布斯 66、Citizen Remote 50+、有机构清单称 73）。：以英国为例，期望未来一年全勤到岗、企业因强制返岗出现离职，说明“灵活/远程”仍是劳动力市场的筹码之一。
IDEA的使用指南
IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
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
339
扫一扫
