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
600+累计阅读
687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散点图绘制详解
Python是一种广泛应用于科学计算、数据分析的编程语言。在数据分析中，散点图是常用的一种可视化方式。本文将详细介绍如何使用Python中的Matplotlib库绘制散点图，并提供相应的源代码。
首先，我们需要安装Matplotlib库。可以使用以下命令进行安装：
pip install matplotlib
接着，我们需要导入Matplotlib库并生成随机数据进行展示。以下是一个简单的例子：
import matplotlib.pyplot as plt
import numpy as np
# 生成随机数据
x = np.random.rand(python制作散点动图
本教程将介绍如何使用Python制作散点动图。我们将通过三个示例代码，从易到难，逐步说明如何使用Python绘制出散点动图。
最佳量化交易的计算机操作系统
本文在Creative Commons许可证下发布 我经常被问到的一个问题是：用哪种计算机操作系统和计算软件进行量化交易的理论研究和代码实现？ 我想把这个问题简化，从挑选的计算软件和计算库来反推最佳使用的计算机操作系统。简而言之，就是在本文撰写的时候而言，如果你想进行基于任何严肃的数学理论和金融量化交易研究理论，这里特指机器学习或者深度学习。并且需要在本地研究机器上使用桌面版本，...
ASME B31.3-2024工艺管道标准介绍
ASME B31.3-2024《过程管道》是ASME发布的最新版工业管道标准，适用于石油、化工、制药等行业的管道系统设计、建造和维护。该版标准进行了多项重要更新，包括软管管理、应力指数调整、新增管件类型、焊接检查和泄漏测试等条款修订，并新增了书面报告要求。标准共594页，为全球过程工业提供管道安全规范，对保障管道系统完整性、可靠性具有重要意义，被多国强制采用或推荐使用。
远程桌面连接失败？手把手教你解决CredSSP加密Oracle漏洞（Win10/Win11通用）
本文详细解析了Windows远程桌面连接因CredSSP加密Oracle漏洞安全更新导致的“身份验证错误”问题。文章从原理入手，提供了通过组策略编辑器（适用于专业版）和注册表编辑器（适用于家庭版）两种核心解决方案，指导用户将安全策略调整为推荐的“缓解”模式，以在保障安全的前提下恢复连接，并给出了高级脚本处理与安全最佳实践。
别再瞎调了！手把手教你搞定STM32步进电机PID速度环（附完整代码与比例系数详解）
本文详细解析了STM32步进电机PID速度环的完整实现过程，从算法输出到脉冲频率的转换链路，重点讲解了比例系数的确定方法及系统标定实验。通过完整的代码示例和调试技巧，帮助工程师解决实际应用中的常见问题，提升步进电机控制精度与稳定性。
荣耀Magic 6 dToF LiDAR Android阵营全球第一首发
40X30分辨率的BSI SPAD小面阵，帧率达到60帧，搭配多传感器融合算法, 可产生足够的稠密点云，为应用设备提供可靠的距离传感。在手机消费电子方案中，dToF LiDAR技术不仅提升了手机的功能和用户体验，还为手机厂商提供了差异化的竞争优势。随着技术的不断进步和市场的快速发展，我们可以期待未来dToF LiDAR技术将在手机领域发挥更大的作用，为用户带来更多创新和便捷的功能。增强现实（AR）体验：通过dToF LiDAR提供的高精度深度信息，手机可以更准确地放置虚拟物体在现实世界的表面上。
强化学习原理及应用作业之动态规划算法【SYSU_2023SpringRL】
本次实践作业将在以下环境进行： 该环境由一个 6×6 网格组成，其中黄色圆圈为智能体出发点，黑色格子为无法通过的墙壁，若智能体向着墙壁方向移动，则会停留在原地，地图边界的移动同理。带有黄色边框的格子为终止状态，即若智能体行动至此状态则整个 episode 结束。红色和绿色表示当前该状态的奖励值，奖励越高则绿色越深，奖励越低则红色越深。状态空间：36（0-35 这 36 个整数，左下角为 0，向右 +1，向上 +6）动作空间：4（0-3 这 4 个整数分别代表左、右、上、下）奖励函数：普通格子 -0.1，到
RHEL 8 与 RHEL 7 与 RHEL 6 的比较，RHEL 8强到离谱！
Red Hat Enterprise Linux（RHEL）是企业级 Linux 发行版中最受欢迎的操作系统之一。RHEL 为服务器和企业环境提供了高性能、稳定性和安全性，适用于各种数据中心和云计算平台。自 RHEL 6 以来，Red Hat 对其操作系统进行了多次重大更新，RHEL 7 和 RHEL 8 带来了许多新的特性、工具和架构变化。
【WRF-Urban】WUDAPT详解：An Urban Weather, Climate, and Environmental Modeling Infrastructure
WUDAPT详解：An Urban Weather, Climate, and Environmental Modeling Infrastructure
C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择（默认选中第一个）2.可录制wmv和avi两种格式的视频并且都带有音频，avi可以录制的分辨率和帧数进行选择，wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照，也可边录制视频边拍照4.添加了视频和音频的压缩功能（酌情使用）这次二次开发发现C#对DirectShow的相关信息太少，这次做完也不敢藏私共享出来，还望大家指正
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
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")
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
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
1525
扫一扫
