<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[小博客大智慧]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/weian4913</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; weian4913]]></copyright><item><title><![CDATA[【环境安装】Dify离线安装]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/145605125</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/145605125</guid><author>weian4913</author><pubDate>Thu, 13 Feb 2025 10:41:30 +0800</pubDate><description><![CDATA[离线安装Dity-0.15.3。]]></description><category></category></item><item><title><![CDATA[【环境安装】重装Docker-26.0.2版本]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/145596903</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/145596903</guid><author>weian4913</author><pubDate>Wed, 12 Feb 2025 17:52:32 +0800</pubDate><description><![CDATA[【Docker离线安装】Linux-Centos7；已有低版本的Docker【目标环境说明】卸载已有Docker，用docker-26.0.2.tgz安装包安装。]]></description><category></category></item><item><title><![CDATA[【环境安装】安装LLaMA-Factory]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/145199994</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/145199994</guid><author>weian4913</author><pubDate>Fri, 17 Jan 2025 10:47:47 +0800</pubDate><description><![CDATA[【机器背景说明】Linux-Centos7；显卡驱动：Driver Version: 460.106.00；【目标环境说明】]]></description><category></category></item><item><title><![CDATA[【语义分割专题文章】]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136524719</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136524719</guid><author>weian4913</author><pubDate>Sat, 09 Mar 2024 14:39:34 +0800</pubDate><description><![CDATA[【语义分割专题文章】]]></description><category></category></item><item><title><![CDATA[【语义分割】【专题系列】三、PSPNet语义分割代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136582303</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136582303</guid><author>weian4913</author><pubDate>Sat, 09 Mar 2024 14:24:55 +0800</pubDate><description><![CDATA[PSPNet语义分割代码实战]]></description><category></category></item><item><title><![CDATA[【语义分割】【专题系列】二、Unet语义分割代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136485707</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136485707</guid><author>weian4913</author><pubDate>Sat, 09 Mar 2024 13:59:47 +0800</pubDate><description><![CDATA[【语义分割】【专题系列】二、Unet语义分割代码实战]]></description><category></category></item><item><title><![CDATA[【OCR专题文章】]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136206350</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136206350</guid><author>weian4913</author><pubDate>Wed, 21 Feb 2024 11:43:44 +0800</pubDate><description><![CDATA[本栏聚焦在OCR的相关算法代码实现]]></description><category></category></item><item><title><![CDATA[【Time Series专题文章】]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136200193</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136200193</guid><author>weian4913</author><pubDate>Wed, 21 Feb 2024 10:00:56 +0800</pubDate><description><![CDATA[综述聚焦在时间序列的相关算法的代码实现]]></description><category></category></item><item><title><![CDATA[【Graph Net专题文章】]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136190460</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136190460</guid><author>weian4913</author><pubDate>Tue, 20 Feb 2024 14:56:17 +0800</pubDate><description><![CDATA[Graph Net专栏文章目录]]></description><category></category></item><item><title><![CDATA[【Time Series】【专题系列】三、Transformer时序预测代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/136108552</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/136108552</guid><author>weian4913</author><pubDate>Tue, 13 Feb 2024 19:16:34 +0800</pubDate><description><![CDATA[本文目标是：通过一个.py文件，以最少第三方依赖库的原则实现“基于Transformer的时间序列预测”任务。]]></description><category></category></item><item><title><![CDATA[【Time Series】【专题系列】二、LSTM时序预测代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/135924189</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/135924189</guid><author>weian4913</author><pubDate>Tue, 30 Jan 2024 03:28:43 +0800</pubDate><description><![CDATA[Time Series之LSTM代码实战]]></description><category></category></item><item><title><![CDATA[【Time Series】【专题系列】一、时序预测股票数据获取]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/126676019</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/126676019</guid><author>weian4913</author><pubDate>Sun, 28 Jan 2024 15:25:33 +0800</pubDate><description><![CDATA[本文主要通过代码，批量获取目标股票的时序数据，为后续时间序列任务做数据准备。]]></description><category></category></item><item><title><![CDATA[【Graph Net】【专题系列】五、GraphSAGE代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/135841489</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/135841489</guid><author>weian4913</author><pubDate>Thu, 25 Jan 2024 12:50:00 +0800</pubDate><description><![CDATA[GraphSAGE是一种高效的图嵌入方法，可以用于不同的图学习任务，如节点分类、链接预测等。它允许学习到一种能够统一不同大小的图、不同类型的图结构以及训练和测试图数据的结构差异的节点嵌入向量。论文链接如下：：为图中每个节点赋予一个初始特征向量，这可以是节点的属性特征，或者简单的one-hot编码。：对于图中的每个节点，从其邻居中随机采样固定数目的节点。采样是为了减少计算资源的消耗，特别是对于那些有很多邻居的节点。采样数目可以是一个超参数。]]></description><category></category></item><item><title><![CDATA[【OCR】【专题系列】二、数据获取-合同数据合成方法]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/133695000</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/133695000</guid><author>weian4913</author><pubDate>Mon, 09 Oct 2023 10:39:36 +0800</pubDate><description><![CDATA[OCR算法在处理合同等文件时，会由于印章等遮挡导致文本误识别。因此在OCR预处理时，有一个很重要的步骤是“去除印章”。其中本文主要聚焦合同印章的数据构建。]]></description><category></category></item><item><title><![CDATA[【Graph Net】【专题系列】二、LINE代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/133152156</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/133152156</guid><author>weian4913</author><pubDate>Fri, 22 Sep 2023 00:09:03 +0800</pubDate><description><![CDATA[本文主要通过代码实战介绍Graph Embedding方法之LINE方法]]></description><category></category></item><item><title><![CDATA[【Graph Net】【专题系列】一、DeepWalk/Node2Vec代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/133082579</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/133082579</guid><author>weian4913</author><pubDate>Thu, 21 Sep 2023 11:45:13 +0800</pubDate><description><![CDATA[本文主要通过代码实战介绍基础的两种图嵌入方式DeepWalk、Node2Vec]]></description><category></category></item><item><title><![CDATA[【Graph Net】【专题系列】四、GAT代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/133045157</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/133045157</guid><author>weian4913</author><pubDate>Tue, 19 Sep 2023 21:25:48 +0800</pubDate><description><![CDATA[基于PyG的GAT代码实战，不说没用的，直接搞代码]]></description><category></category></item><item><title><![CDATA[【Graph Net】【专题系列】三、GNN/GCN代码实战]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/132991337</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/132991337</guid><author>weian4913</author><pubDate>Tue, 19 Sep 2023 00:36:56 +0800</pubDate><description><![CDATA[GNN/GCN两个基础图深度学习网络算法实现。本文目标：不用PyG，只为打代码基础。]]></description><category></category></item><item><title><![CDATA[【OCR】【专题系列】五、基于Encoder-Decoder的文本识别]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/126228281</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/126228281</guid><author>weian4913</author><pubDate>Mon, 29 Aug 2022 19:54:16 +0800</pubDate><description><![CDATA[基于Encoder-Attention-Decoder的端对端文本识别]]></description><category></category></item><item><title><![CDATA[【OCR】【专题系列】四、基于RCNN-CTC的不定长文本识别]]></title><link>https://blog.youkuaiyun.com/weian4913/article/details/121212660</link><guid>https://blog.youkuaiyun.com/weian4913/article/details/121212660</guid><author>weian4913</author><pubDate>Mon, 08 Aug 2022 15:13:50 +0800</pubDate><description><![CDATA[本文实现基于crnn-ctc的不定长文本识别，采用pytorch框架可直接运行跑通，并基于开源数据集进行实验验证。]]></description><category></category></item></channel></rss>