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开篇寄语
面试不必死记硬背，带着GPT去面试简直降维打击！
为您推荐一款颠覆性的GPT面试助手软件：“会议面试精灵”。这款工具能够实时分析语音内容、智能识别面试问题并生成精准答案，让您轻松应对八股文题目，从容处理各类复杂面试问题。
毕竟八股文在工作中很少使用，仅作为面试的筛选工具，不值得投入过多的精力。
如何获取“会议面试精灵”？
- 搜索引擎中输入关键词：“会议面试精灵”（记得加上双引号）进行搜索。
- 直接访问下载链接：https://www.interviewpass.fun
目录
【一线大厂Java面试题合集】第54篇-Kafka核心架构与原理
Producer: acks=all + retries + 幂等性↓↓Consumer: 关闭自动提交 + 业务处理成功后手动提交 + 幂等消费面试技巧：强调"消息不丢失"是三端配合的结果，缺一不可。同时提到 acks=all 必须配合 min.insync.replicas，否则当 ISR 只剩 Leader 一个时，等于退化为 acks=1。
win10自动修复-无限循环【通用完美】超简单解决办法
2020.02.14今个我的笔记本电脑（Thinkpad Carbon 7 X1）（win10）早晨起来发现蓝屏，上面显示自动修复失败，重启后依然自动修复失败，不断手动重启，始终是修复失败。然后上网开始搜集资料找解决办法。 前边介绍关闭自动修复，如何找出错文件的方法；如果想看最终解决办法，直接看第三部分 上网就会发现这种现象很常见，同时是电脑自动安装系统更新，或者安装某些驱动出现。解决办法也大...
RML2016.10a （CNN、LSTM、CNN-LSTM、CNN-LSTM-Multi-head-attention, TensorFlow）
RML2016.10a （CNN、LSTM、CNN-LSTM、CNN-LSTM-Multi-head-attention, TensorFlow） 包含所有数据和代码，支持一键运行（jupyter）
Ubuntu 解决WPS缺少宋体，微软雅黑等中文字体
这篇博客解决Ubuntu 18.04 无法使用中文字体的问题 问题描述
EVE-NG 新增 qcow2 镜像教程
本文介绍了如何在 EVE-NG 网络仿真平台中添加 QCOW2 镜像并配置 YML 文件。首先，用户需要了解 EVE-NG 的目录结构，上传并重命名 QCOW2 镜像，然后设置文件权限并刷新界面以识别设备。接着，讲解了 YML 文件的作用，它用于定义设备的CPU、内存、网卡等参数，并给出了配置示例。若有多个设备版本，可以通过复制并重命名 YML 文件使其独立显示。最后，通过检查 QEMU 进程和日志验证 YML 配置是否生效。总结部分强调了 EVE-NG 的自动识别机制以及独立管理设备版本的方式。
TC3XX - MCAL知识点（十三）：IOM EB-tresos配置说明及代码浅析
输入输出监视器(IOM)驱动程序作为比较单元，检查系统外设输出的正确操作，这些输出可以作为监视功能的输入。监控功能需要通过配置IOM硬件来实现。它向SMU生成全局系统事件。IOM驱动程序初始化并控制微控制器的IOM单元。驱动程序还为用户提供初始化和设置IOM内部单元的阈值的服务。它还应该提供重置IOM内核的服务。应该提供该服务来组合单个或多个本地事件，以生成单个全局系统事件。IOM驱动程序严重依赖于初始化和配置。软硬件关系如下图缩写解释LAM逻辑分析模块ECM事件组合模块FPC。
大模型安全研究论文整理 2026年版
知乎博主小红书博主GitHub数据集参考： 大模型越狱指令 (harmful questions) 数据集整理会思考的大模型更不听话，我的豆包失控了…让QwQ思考模型-不思考的小技巧qwen3的致命幻觉！大模型微调所有大模型都在讨好人类不要思考过程，推理模型能力能够更强丨UC伯克利等最新研究o3/o4-mini幻觉暴增2-3倍！OpenAI官方承认暂无法解释原因UC伯克利：让推理模型少思考，准确率反而更高了！一句话让DeepSeek思考停不下来，北大团队：这是针对AI的DDoS攻击慢思考准确率反降30%！普
Android Input——输入系统介绍（一）
Input 系统对于提供流畅、灵敏和一致的用户交互体验至关重要，它需要在不同的设备、场景和应用中保证输入事件的正确性、及时性和安全性。Android 输入系统是一个复杂但高效的设计，旨在处理来自各种输入设备（如触摸屏、键盘等）的用户交互，并将这些交互转换为应用程序可以理解和响应的形式。EventHub：输入系统的最底层，直接与硬件通信，监听和读取输入设备的原始事件。：从 EventHub 获取原始输入事件，对其进行解析，转化为更高层次的事件，以便上层应用或服务能够理解。
Claude Code 新手安装指南，手把手教你怎么安装使用
如果你是企业内网用户，或者需要自定义 API 端点，那可以用手动配置 API Key 的方式。
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
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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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