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
282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
本文聚焦于大型语言模型（LLMs）微调对个体响应的影响，提出了一种量化微调贡献的新方法——Tuning Contribution（TuCo）。
LLM Training
当前文章被收录于：
LLM Training
主要是LLM训练相关的paper，针对训练加速，微调等相关内容。涉及的关键词包括finetuning以及training。
AI 应用开发：四维架构落地实战——以 HR Agent 为例拆解感知·推理·记忆·执行【意图路由、规划编排、长期记忆、工具执行、面试题】
本文基于四维架构（感知·推理·记忆·执行），以HR Agent为例，系统拆解大模型应用从“聊天”到“生产可用”的落地路径。通过Spring AI实现意图路由、规划编排、RAG检索与工具调用，结合真实踩坑复盘，揭示架构化设计对安全、成本与可维护性的关键价值，为AI Agent工程化提供可复用的实战框架。
一文讲透3GPP TS 36.104发射链路相关指标
3GPP是制定移动通信标准的国际组织，其36.104规范对LTE基站射频性能提出详细要求。规范主要涵盖七方面：1)基站输出功率分类标准；2)LTE信道带宽计算方法；3)TD-LTE特有的发射机关断功率及时延要求；4)信号质量指标（频率误差和EVM）及其影响因素；5)邻道泄漏比(ACLR)的45dBc标准；6)带内频谱辐射模板(OBUE)的噪声限制；7)9kHz-12.75GHz频段的带外杂散辐射要求。这些技术要求保障了LTE基站的射频性能和网络质量。
从零开始的 AI Infra 学习之路
AI Infra（AI 基础设施）是指为上层的 AI 算法应用提供支持的 AI 全栈底层技术，通过合理利用计算机体系结构，可以实现 AI 计算的加速和部署。AI Infra 主要包括以下内容：AI 训练框架 & 推理引擎；AI 编译 & 计算架构；AI 硬件 & 体系结构。参考资料：；。
Wonderware Intouch 2014R2 SP1 安装教程
下载Wonderware System Platform 2014R2 SP1 and Intouch 2014R2 SP1的安装压缩包，如下 软件的下载连接地址如下： https://pan.baidu.com/s/1a2GnI_nfT3vK1l49B94FIQ， 提取码：6ec6 或者直接微信扫描下面的二维码 管理员运行下面的setup.exe可执行程序， 进入安装，选择Product Based Selection, 自定义选择需要安装的程序，如下： 普通的Into
Eclipse 数据空间组件（EDC）项目介绍
在当今数字经济的背景下，数据成为了新的“石油”。但与石油不同的是，数据可以无限共享且再生。然而，如何在保持数据主权的前提下实现高效、安全的数据共享，成为了企业和组织面临的主要挑战。这时，**Eclipse 数据空间组件（Eclipse Dataspace Components, EDC）**应运而生，它为企业在多方参与的数据交换中提供了一个标准化、可扩展的解决方案。数据空间不仅是一个多组织间的数据共享协议，还是一套支撑这些协议运行的技术基础设施。
2025年软件测试职业院校技能大赛—ERP管理平台-基础信息管理商品管理测试用例
竞赛对接软件测试相关标准，以“ERP管理平台” 为被测系统，围绕软件测试领域的主流技术及工具，考查选手面 对实际问题的综合分析能力，对测试文档、测试用例的设计能力， 对单元测试、自动化测试、性能测试、接口测试等测试工具及方 法的掌握程度，检验选手团队协作能力、组织规划能力、方法运用能力等。
高项备考论文：规划绩效域
高项备考论文：规划绩效域
【Bluetooth蓝牙开发】一、开篇词 | 打造全网最详细的Bluetooth开发教程
1、开篇词 2、蓝牙开发入门 3、蓝牙协议栈总览 4、蓝牙协议栈——物理层 5、蓝牙协议栈——链路层 6、蓝牙协议栈——传输层 7、蓝牙协议栈——L2CAP协议 8、蓝牙协议栈——ATT协议 9、蓝牙协议栈——GATT协议 10、蓝牙通信流程分析 11、蓝牙协议栈——Bluez交叉编译 12、蓝牙调试工具汇总
大模型自我认知微调的实战教程
本文主要介绍了大模型自我认知微调的实战教程，希望对学习大语言模型的同学们有所帮助。微调成功并保留原有能力的截图如下所示： 文章目录 1. 前言 2. 配置环境 2.1 安装虚拟环境 2.2 安装依赖库 3. 实战代码 3.1 配置数据集 3.2 lora微调
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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。
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
超级会员免费看
订阅专栏 解锁全文
740
扫一扫
