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本文是LLM系列文章，针对《RecurrentGemma: Moving Past Transformers for Efficient Open Language Models》的翻译。
我们介绍RecurrenceGemma，这是一个使用谷歌新颖的Griffin架构的开放语言模型。Griffin将线性递归与局部注意力相结合，在语言方面取得了出色的表现。它具有固定大小的状态，这减少了内存使用，并能够对长序列进行高效推理。我们提供了一个具有2B个非嵌入参数的预训练模型和一个指令调优变体。尽管在较少的token上进行了训练，但这两种模型都实现了与Gemma-2B相当的性能。
RecurrenceGemma-2B提供了Gemma的性能，同时在推理过程中实现了更高的吞吐量，特别是在长序列上。我们希望RecurrenceGemma能够在资源受限的环境中解锁高性能小型语言模型的新应用。
Windows平台无人机仿真：从零搭建SITL环境与QGC联调
本文详细指导了在Windows平台上从零搭建无人机软件在环（SITL）仿真环境，并与QGroundControl（QGC）进行联调的完整流程。通过使用Cygwin工具链，开发者可以在没有实体硬件的情况下，模拟运行ArduPilot飞控，并通过MAVLink协议连接QGC地面站进行全功能测试，为无人机软件开发提供了一个安全、经济的虚拟沙盒环境。
STM32F103移植NuttX实战：从零搭建最小系统到串口Shell（附完整配置文件）
本文详细介绍了在STM32F103C8T6开发板上移植NuttX实时操作系统的完整过程，包括环境搭建、BSP配置、系统时钟初始化、驱动裁剪、编译烧录及串口Shell交互。通过实战指南帮助开发者快速掌握NuttX移植技巧，解决典型问题，适用于嵌入式系统开发。
GEOS-Chem笔记——模式下载安装+驱动数据+运行流程
看过我博客的会发现我写了很多CESM的笔记，但是说实话，平时科研我其实更多时候是GEOS-Chemuser。之所以写了很多CESM笔记，其实是因为不会，就边学边写。然后GEOS-Chem已经用比较熟了，所以也没想过做笔记啥的。但是我发现越来越多朋友私信我各种CESM问题，我觉得我可能给大家营造了一种我是CESM达人的错觉。感觉自己有点跑偏了呢。公平起见，我觉得我有必要补充点GEOS-Chem笔记。 GEOS-Chem是研究大气化学的人常用的大气化学传输模式，在全世界范围的大气化学研究组中被广泛使用。不过.
【乐企-工具篇】生成发票左上角二维码
【代码】【乐企-业务篇】生成发票左上角二维码。
手把手教你学simulink（24.4）--Simulink在机器人视觉场景下的应用——基于视觉的物体跟踪与抓取
该系统集成了图像处理、目标检测、运动估计、路径规划、抓取控制和视觉反馈等多个功能模块，能够实现实时处理视觉数据、检测并跟踪移动物体、规划最优路径并控制机械臂进行精确抓取。本项目的目标是使用Simulink和MATLAB设计一个基于视觉的物体跟踪与抓取系统，该系统能够实时处理图像数据，检测并跟踪移动物体，规划最优的抓取路径，并控制机械臂进行精确抓取。为了验证系统的性能，我们在Simulink中搭建了基于视觉的物体跟踪与抓取系统的仿真模型。
15、降维算法：原理、实现与应用
本文深入介绍了多种降维算法，包括线性方法如主成分分析（PCA）和线性判别分析（LDA），以及非线性方法如Sammon映射、t分布随机邻域嵌入（t-SNE）和自编码器。文章通过C++库Dlib和Shogun展示了这些算法的具体实现，并结合图像压缩和数据可视化等实际案例，分析了不同算法的适用场景。此外，还探讨了降维算法的选择策略、优化改进方法以及未来发展趋势，为读者提供了全面的技术理解和应用指导。
5步上手Quartz.NetUI：.NET Core定时任务的开箱即用解决方案
Quartz.NetUI是一个基于.NET Core + Quartz.NET + Vue + IView的开箱即用的定时任务UI解决方案。它结合了Quartz.NET的强大功能和Vue.js的易用性，提供了一个不依赖数据库、只需在界面进行简单配置即可使用的定时任务管理系统。通过上述步骤，你已经学会了如何使用Quartz.NetUI在.NET Core项目中实现定时任务。Quartz.NetUI不仅功能强大，而且使用简单，非常适合那些需要快速实现定时任务管理的开发者。
手把手教你学PCIE--PCIe架构详解 - 物理层结构
在前一篇文章中，我们对PCIe与其他总线协议进行了对比分析。本文将深入探讨PCIe的物理层结构，这是PCIe协议的核心组成部分之一。物理层负责实际的电气信号传输，理解其工作原理对于设计和调试PCIe系统至关重要。PCIe的物理层是协议栈的最底层，负责将数据从链路层转换为电信号进行传输，并在接收端将电信号还原为数据。差分信号传输：通过一对差分信号线实现高速、低噪声的数据传输。时钟恢复：从数据流中提取时钟信号以同步收发双方。均衡与预加重：补偿信号在传输过程中的损耗，提高链路可靠性。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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。
VC中sqlite的使用
在VC中对sqlite的使用，实现数据库大批数据的快速插入，读取数据库中的数据在ListCtrl中显示
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