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Gemini是由Google开发的一系列先进的大型语言模型，旨在执行多种任务，包括但不限于自然语言理解、生成、编程、逻辑推理以及多模态处理。以下是关于Gemini的一些关键信息：
Gemini模型分为不同的版本，比如Gemini Nano、Gemini Pro和Gemini Ultra，它们分别适用于不同复杂度的任务和设备环境。
Gemini 2.5 Pro引入了多项技术创新，特别是在推理能力、支持更长的上下文长度及深度研究功能方面进行了显著优化。
Gemini Deep Research是一个基于Gemini 2.5 Pro构建的专业级智能代理，能够战略性地浏览互联网，针对用户提出的高度专业化问题提供有依据的答案和支持。
Google还推出了Gemini Advanced服务，这是基于Gemini Ultra模型的一种高级订阅方案，面向需要强大AI能力和深入分析的企业和个人用户提供。
Gemini不仅限于文本交互，在图像识别和其他感官输入整合上也表现出色，展现了强大的跨模式理解和响应能力。
Gemini：AI 人工智能的技术变革先锋
在人工智能领域，技术的飞速发展促使各大科技公司不断投入资源研发更强大的模型。Gemini 的诞生目的在于突破现有大模型的局限性，提供更强大、更智能、更具通用性的人工智能解决方案。其范围涵盖了自然语言处理、图像识别、语音交互、多模态处理等多个领域，旨在为各个行业提供高效、准确的智能服务。本文将首先介绍 Gemini 的核心概念与联系，包括其架构和原理；接着阐述核心算法原理并给出具体操作步骤，同时使用 Python 代码进行详细说明；然后介绍数学模型和公式，并结合实例进行讲解；
【大模型应用开发】学习导读列表--建议收藏
该专栏是一个持续更新的大模型应用开发系统教程，涵盖从Python基础到前沿AI工具实战的完整学习路径。内容分为十六大模块：Python基础（语法、数据类型、Conda等）、LangChain框架、FastAPI后端开发（含JWT鉴权、SSE流式输出）、PostgreSQL数据库、Pandas数据清洗、ElasticSearch搜索、大模型理论知识、多模型API调用实战（DeepSeek、智普、通义千问等）、Dify平台部署、LORA微调技术，以及OpenClaw、Hermes、Claude Code
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自动化登录账号，读取数据表中内容去填写信息去查询，并判断多个数据不同的页面去不同的读取查询结果内容，如考生各项信息与各科成绩，总分和查询时间，采集出来到指定的数据表格中，等待时间并依次循环运行。主要功能。
全面分析总结解决Could not transfer artifact org.apache.maven.plugins等此类问题的方法
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本文是一份详细的OpenMMLab实战指南，重点介绍了如何从零开始搭建环境并运行首个目标检测模型。内容涵盖Miniconda环境配置、PyTorch与CUDA版本匹配、使用MIM工具安装MMCV和MMDetection的核心步骤，并通过RTMDet-tiny模型演示了完整的推理流程，帮助开发者高效入门这一计算机视觉开源算法体系。
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这次实验利用logisim进行设计，进行了以下这四个实验，其中分为汉字国标码转区位码实验、汉字机内码获取实验、海明编码电路设计与海明解码，实验报告分为四个部分，在这四个部分中依次对四个实验的方案设计，设计思路进行了介绍
国密系列算法简介
众所周知，为了保障商用密码的安全性，国家商用密码管理办公室制定了一系列密码标准，包括SM1（SCB2）、SM2、SM3、SM4、SM7、SM9、祖冲之密码算法（ZUC)那等等。其中SM1、SM4、SM7、祖冲之密码（ZUC）是对称算法；SM2、SM9是非对称算法；SM3是哈希算法。目前，这些算法已广泛应用于各个领域中，期待有一天会有采用国密算法的区块链应用出现。其中SM1、SM7算法不公开，调用该...
Nvidia Isaac Sim图形界面 入门教程 2024（3）
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一文搞定离线环境下的RAG引擎部署
在人工智能浪潮下，技术已成为构建智能问答、文档分析和代码助手等应用的核心。然而，在许多企业场景中，出于数据安全和性能的考虑，服务器往往部署在中，并要求极致的推理效率。这给依赖外部资源的应用部署带来了巨大挑战。如何在这种严苛的环境下，完整、可靠地部署一套包含向量数据库、**高性能推理服务（vLLM）**和Python后台的复杂RAG系统呢？本文将为你提供一个，手把手教你如何使用 Docker 技术，将一套基于 Weaviate 和的 Python RAG 应用完整打包，并在离线环境中一键部署。
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
怀旧版电脑GBC模拟器
再现原版GBC掌机的界面，电脑运行连游戏界面都一样大小，很怀旧，很真实，（英文版）强烈推荐，很小的文件，很难才找到的。
LinePhone3.2.7(android端最新版本) sip客户端
SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/
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