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<!DOCTYPE html><html lang="zh-CN" data-default-color-scheme="auto"><head><meta charset="UTF-8"><link rel="apple-touch-icon" sizes="76x76" href="/img/fluid.png"><link rel="icon" href="/source/img/fluid.png"><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=5,shrink-to-fit=no"><meta http-equiv="x-ua-compatible" content="ie=edge"><meta name="theme-color" content="#2f4154"><meta name="author" content="Jimes"><meta name="keywords" content=""><meta property="og:type" content="website"><meta property="og:title" content="马锦的博客"><meta property="og:url" content="https://jimes.cn/index.html"><meta property="og:site_name" content="马锦的博客"><meta property="og:locale" content="zh_CN"><meta property="article:author" content="Jimes"><meta name="twitter:card" content="summary_large_image"><meta name="referrer" content="no-referrer-when-downgrade"><title>马锦的博客</title><link rel="stylesheet" href="https://lib.baomitu.com/twitter-bootstrap/4.6.1/css/bootstrap.min.css"><link 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target="_self" href="javascript:;" aria-label="Color Toggle"><i class="iconfont icon-dark" id="color-toggle-icon"></i></a></li></ul></div></div></nav><div id="banner" class="banner" parallax="true" style="background:url(/img/default.png) no-repeat center center;background-size:cover"><div class="full-bg-img"><div class="mask flex-center" style="background-color:rgba(0,0,0,0)"><div class="banner-text text-center fade-in-up"><div class="h2"><span id="subtitle" data-typed-text="学海无涯，舟行天下"></span></div></div><div class="scroll-down-bar"><i class="iconfont icon-arrowdown"></i></div></div></div></div></div></header><main><div class="container nopadding-x-md"><div id="board" style="margin-top:0"><div class="container"><div class="row"><div class="col-12 col-md-10 m-auto"><div class="row mx-auto index-card"><article class="col-12 col-md-12 mx-auto index-info"><h2 class="index-header"><a href="/2025/03/22/%E8%80%83%E7%A0%94%E5%A4%8D%E8%AF%95/" target="_self">考研复试</a></h2><a class="index-excerpt index-excerpt__noimg" href="/2025/03/22/%E8%80%83%E7%A0%94%E5%A4%8D%E8%AF%95/" target="_self"><div>宁波大学计算机科学与技术机试、综合面试。</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2025-03-22 23:58" pubdate>2025-03-22</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/%E8%80%83%E7%A0%94/" class="category-chain-item">考研</a></span></span></div></div></article></div><div class="row mx-auto index-card"><article class="col-12 col-md-12 mx-auto index-info"><h2 class="index-header"><a href="/2025/02/14/GNN/" target="_self">图神经网络-GNN</a></h2><a class="index-excerpt index-excerpt__noimg" href="/2025/02/14/GNN/" target="_self"><div>图神经网络（GNN）数学公式解析图神经网络（Graph Neural Networks, GNN）是一类专门用于处理图结构数据的深度学习模型。GNN 的核心思想是信息传递（Message Passing）：通过迭代地在节点之间传递和聚合邻居的信息，逐步更新节点的表示，最终用于节点分类、链接预测或图分类等任务。 1. 图与初始表示设图为：$$\mathcal{G} = (\mathcal</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2025-02-14 15:23" pubdate>2025-02-14</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/" class="category-chain-item">人工智能</a></span></span></div></div></article></div><div class="row mx-auto index-card"><article class="col-12 col-md-12 mx-auto index-info"><h2 class="index-header"><a href="/2025/02/08/RNN/" target="_self">从RNN到LSTM（包含代码实现）</a></h2><a class="index-excerpt index-excerpt__noimg" href="/2025/02/08/RNN/" target="_self"><div>循环神经网络。</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2025-02-08 22:00" pubdate>2025-02-08</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/" class="category-chain-item">人工智能</a></span></span></div></div></article></div><div class="row mx-auto index-card"><article class="col-12 col-md-12 mx-auto index-info"><h2 class="index-header"><a href="/2025/01/26/pytorch%E6%95%B0%E6%8D%AE%E9%9B%86%E4%BB%A5%E5%8F%8A%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86/" target="_self">pytorch数据集与数据预处理</a></h2><a class="index-excerpt index-excerpt__noimg" href="/2025/01/26/pytorch%E6%95%B0%E6%8D%AE%E9%9B%86%E4%BB%A5%E5%8F%8A%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86/" target="_self"><div>一. PyTorch 内置数据集PyTorch 通过 torchvision.datasets 模块提供了许多常用的数据集，例如： MNIST：手写数字图像数据集，用于图像分类任务。 CIFAR：包含 10 个类别、60000 张 32x32 的彩色图像数据集，用于图像分类任务。 COCO：通用物体检测、分割、关键点检测数据集，包含超过 330k 个图像和 2.5M 个目标实例的大规模数据集。</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2025-01-26 22:36" pubdate>2025-01-26</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/" class="category-chain-item">人工智能</a></span></span></div></div></article></div><div class="row mx-auto index-card"><article class="col-12 col-md-12 mx-auto index-info"><h2 class="index-header"><a href="/2025/01/26/pytorch%E7%9A%84torch/" target="_self">pytorch基础--torch</a></h2><a class="index-excerpt index-excerpt__noimg" href="/2025/01/26/pytorch%E7%9A%84torch/" target="_self"><div>PyTorch 的 torch 模块torch 是 PyTorch 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