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内容太多就会不好理解，所以每一篇都只有一两个知识点，争取5分钟之内打完收工，欢迎大家使用量子学习法：
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ArcPy中（矢量类）的空间数据的结构
矢量类空间数据是针对栅格类空间数据来说的，后者我们后续有专门的章节论述。
在ArcPy中，矢量（空间）数据是一种统称，它一般是由空间部分和属性部分组成:
记录的是几何信息，在物理上表现为二进制类型的字段，例如在Oracle数据库里面，表现为Long Raw类型（Oracle Spatial有个专门的类型，叫做SDO_Geometry），而在OGC标准里面，有一个专门的类型名称，就是Geometry。
在ArcGIS/GeoScene，用的也是Geometry大类（ESRI来就是OGC的发起方之一，不够SDE库里面，类型会叫做ST_Geometry），不过在存储上（例如Shapefile文件，或者GDB里面），名称一般表现为Shape字段（也是我们经常口语中经常把空间字段称之为“shape字段”）。
存储表达
下面我们来空间数据看看在不同平台
Follow Me: ArcPy(1)
做GIS这么多年了，说来惭愧，用ESRI产品还真不多。下面，让我来对ESRI的新产品ArcPy，进行一番深度吐槽！
【ArcPy】Python脚本实现空间数据的自动化压缩与优化
包含几何信息和属性数据的文件格式。：Esri的数据库格式，用于存储空间数据。Raster数据：包括TIFF、JPEG、GRID等格式，用于存储栅格数据。通过本文的介绍，你已经学会了如何使用Python和arcpy实现空间数据的自动化压缩与优化。这些技巧将帮助你更高效地管理空间数据，节省存储空间，并提高数据处理和访问的效率。未来，你可以继续探索arcpy的其他功能，如空间数据分析、数据转换和自动化制图等。掌握这些技能，将使你在GIS领域的工作更加游刃有余。
ArcPy-6.探索空间数据
本章介绍了几种探索空间数据的方法，包括检查数据集是否存在、描述数据集以及在工作空间中列出数据集。列表函数不仅用于列出数据集，还用于列出工作空间、字段和表等元素。内置 Python 函数可用于操作列表。列表在脚本中很常见，因为它们可以迭代元素以自动化工作流程。
[QDS]从零开始，写第一个Qt Design Studio到程序调用的项目
最近在使用Qt Design Studio进行开发，但是简中网上要不就是只搜得到Qt Designer(Qt Creator内部库)，要不就只搜得到一点营销号不知道从哪里搬来的账号，鉴于Qt Design Studio是一个这么强大的软件，自然是需要来进行一下小小的实践的。所以开了这篇文章，方便没有使用过Qt Design Studio 和 QML的开发者也可以快速上手QDS(Qt Design Studio)。在本文中，你将学会如何使用QDS开发一个简单的QML程序，与你的C++后端代码进行交流。
别再翻手册了！用Materials Studio的Forcite模块，5步搞定复杂溶剂的溶解度参数计算
本文详细介绍了如何使用Materials Studio的Forcite模块，通过5个步骤快速计算复杂溶剂的溶解度参数。从分子建模、结构优化到分子动力学采样，再到最终的内聚能密度计算，该方法大幅提升了石油化工和高分子材料研发中的效率，避免了传统手册查阅和实验的繁琐过程。
LabView实现modbus通讯
labview实现modbus通讯
ARM双核锁步DCLS Lockstep技术研究（FPGA实现）
基于《Applying Dual-Core Lockstep in Embedded Processors to Mitigate Radiation-induced Soft Errors》文章总结 RISC-V双核锁步DCLS Lockstep技术参考之前文章： RISC-V双核锁步DCLS Lockstep技术总结_远古架构师alanwu的博客-优快云博客https://blog.youkuaiyun.com/heyuming20062007/article/details/123329537ARM+RIS
20道SpringSecurity常见面试题汇总（超详细回答 ！！！）
本文中，我们将探讨一些与 Spring Security 相关的面试问题。Spring Security 是一款流行的 Java 应用程序安全框架，它提供了一套全面且易于使用的安全解决方案。
Q3D提取PCB走线RLCG参数
介绍一种提取PCB走线寄生电容和寄生电感的方法
SAP 系统里生成 PDF 的几条主干路径：从 Spool 转换到 Adobe 原生渲染，再到 BTP 云服务
本文系统梳理了SAP生态中PDF生成的多种技术方案，包括Spool转换、OTF转换、Smart Forms/Adobe Forms原生生成以及云端服务等不同路径。文章通过实际案例和代码示例，详细分析了各方案的适用场景、技术要点和工程实践建议，特别强调了多语言支持、字体处理、性能优化等关键因素。针对不同业务需求，提供了从简单导出到复杂交互式PDF的完整解决方案框架，为SAP环境下的PDF处理提供了实用指南。
合肥工业大学操作系统实验8
依据操作系统课程所介绍的地址映射与内存共享的原理，本实验的目的是深入理解物理内存的分页管理方式。 深入理解操作系统的段、页式内存管理。包括理解段表、二级页表，以及逻辑地址、线性地址、 物理地址的映射过程。编程实现段、页式内存管理上的内存共享，从而深入理解操作系统的内存管理。从而加深对教材上地址映射与内存共享的理解。
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
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")
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