<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[有一分热，发一分光。]]></title><description><![CDATA[没更就是在看论文(doge]]></description><link>https://blog.youkuaiyun.com/qq_45034708</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; qq_45034708]]></copyright><item><title><![CDATA[ubuntu创建pytorch-gpu的docker环境]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/135372537</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/135372537</guid><author>qq_45034708</author><pubDate>Thu, 04 Jan 2024 18:55:41 +0800</pubDate><description><![CDATA[使用docker的好处就是可以将你的环境和别人的分开，特别是共用的情况下。本文介绍了ubuntu环境下创建pytorch-gpu的教程，centos其实也是差不多的。]]></description><category></category></item><item><title><![CDATA[Friedman检验及后续Nemenyi检验可视化]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/135309225</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/135309225</guid><author>qq_45034708</author><pubDate>Mon, 01 Jan 2024 23:13:34 +0800</pubDate><description><![CDATA[每篇论文都可以加的数值分析实验！]]></description><category></category></item><item><title><![CDATA[解决Github无法上传＞100M文件——只需两行代码]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/130746283</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/130746283</guid><author>qq_45034708</author><pubDate>Fri, 29 Dec 2023 18:10:22 +0800</pubDate><description><![CDATA[this exceeds GitHub's file size limit of 100.00 MB]]></description><category></category></item><item><title><![CDATA[Pattern Recognition投稿经验]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/129291964</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/129291964</guid><author>qq_45034708</author><pubDate>Sat, 18 Nov 2023 13:01:29 +0800</pubDate><description><![CDATA[停更了大半年，近期终于完成了论文投稿，趁我还记得分享一下投稿经验，顺便给我的论文积点功德(bushi]]></description><category></category></item><item><title><![CDATA[范数详解-torch.linalg.norm计算实例]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/130455015</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/130455015</guid><author>qq_45034708</author><pubDate>Mon, 01 May 2023 11:54:45 +0800</pubDate><description><![CDATA[本文以torch.linalg.norm()，详细讲解二范数、F范数、核范数、无穷范数、L1范数、L2范数的定义和计算。]]></description><category></category></item><item><title><![CDATA[三周年创作纪念日]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/129773436</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/129773436</guid><author>qq_45034708</author><pubDate>Sat, 25 Mar 2023 23:19:09 +0800</pubDate><description><![CDATA[有一分热，发一分光，就令萤火一般，也可以在黑暗里发一点光，不必等候炬火。]]></description><category></category></item><item><title><![CDATA[Mathematics2022-Network Embedding Algorithm Taking in Variational Graph AutoEncoder]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/129682887</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/129682887</guid><author>qq_45034708</author><pubDate>Fri, 24 Mar 2023 08:53:16 +0800</pubDate><description><![CDATA[研读《基于变分自编码器的图嵌入算法》——Chen, Dongming, et al. "Network embedding algorithm taking in variational graph autoencoder." Mathematics 10.3 (2022): 485.]]></description><category></category></item><item><title><![CDATA[KDD2016-Structural Deep Network Embedding]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/129470035</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/129470035</guid><author>qq_45034708</author><pubDate>Tue, 14 Mar 2023 21:33:20 +0800</pubDate><description><![CDATA[研读《结构化深度图嵌入》——Wang D, Cui P, Zhu W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016: 1225-1234.]]></description><category></category></item><item><title><![CDATA[AAAI2021-Hierarchical Reinforcement Learning for Integrated Recommendation]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/129411956</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/129411956</guid><author>qq_45034708</author><pubDate>Wed, 08 Mar 2023 23:53:24 +0800</pubDate><description><![CDATA[研读《基于分层强化学习的综合推荐》——Xie R, Zhang S, Wang R, et al. Hierarchical reinforcement learning for integrated recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(5): 4521-4528.]]></description><category></category></item><item><title><![CDATA[深度学习-AlexNet（第一个深度卷积网络）]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/128438794</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/128438794</guid><author>qq_45034708</author><pubDate>Sun, 25 Dec 2022 22:12:29 +0800</pubDate><description><![CDATA[AlexNet是第一个深度卷积网络模型，赢得了2012年ImageNet图像分类竞赛的冠军，自98年的LeNet后再次将深度学习研究引热，创造性的提出了很多方法且影响至今，如使用GPU进行训练，使用ReLU 作为非线性激活函数，使用 Dropout 防止过拟合，使用数据增强来提高模型准确率等。]]></description><category></category></item><item><title><![CDATA[深度学习-LeNet（第一个卷积神经网络）]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/128319241</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/128319241</guid><author>qq_45034708</author><pubDate>Thu, 15 Dec 2022 15:56:11 +0800</pubDate><description><![CDATA[LeNet模型是在1998年提出的一种图像分类模型，应用于支票或邮件编码上的手写数字的识别，也被认为是最早的卷积神经网络(CNN)，为后续CNN的发展奠定了基础，作者LeCun Y也被誉为卷积神经网络之父。]]></description><category></category></item><item><title><![CDATA[深度学习-神经网络（Pytorch应用）]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/128258328</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/128258328</guid><author>qq_45034708</author><pubDate>Sun, 11 Dec 2022 17:56:16 +0800</pubDate><description><![CDATA[神经网络中的隐含层可以分为卷积层、池化层、激活层、线性层等，本文将介绍相关原理及Pytorch中代码应用。]]></description><category></category></item><item><title><![CDATA[深度学习-Tensorboard可视化面板]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/128212320</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/128212320</guid><author>qq_45034708</author><pubDate>Thu, 08 Dec 2022 00:40:57 +0800</pubDate><description><![CDATA[Tensorboard是Tensorflow官方提供的实用可视化工具，可以将模型训练过程中的各种数据保存到本地，然后在web端可视化展现这些信息，直观便捷的方便我们进行优化调试。]]></description><category></category></item><item><title><![CDATA[集成学习-Bagging和Boosting算法]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127565611</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127565611</guid><author>qq_45034708</author><pubDate>Fri, 18 Nov 2022 12:13:35 +0800</pubDate><description><![CDATA[本文介绍了集成学习中的Bagging和Bosting算法，包括随机森林、AdaBoost、GDRT、XGBoost的代码应用。]]></description><category></category></item><item><title><![CDATA[机器学习-主成分分析PCA降维]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127849293</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127849293</guid><author>qq_45034708</author><pubDate>Mon, 14 Nov 2022 21:49:32 +0800</pubDate><description><![CDATA[主成分分析（Principle Component Analysis，PCA）是常用的降维方法，用较少的互不相关的新变量来反映原变量所表示的大部分信息，有效解决维度灾难问题。]]></description><category></category></item><item><title><![CDATA[机器学习-层次聚类（谱系聚类）算法]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127743514</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127743514</guid><author>qq_45034708</author><pubDate>Tue, 08 Nov 2022 11:56:11 +0800</pubDate><description><![CDATA[层次聚类（Hierarchical Clustreing）又称谱系聚类，通过在不同层次上对数据集进行划分，形成树形的聚类结构。很好体现类的层次关系，且不用预先制定聚类数，对大样本也有较好效果。]]></description><category></category></item><item><title><![CDATA[机器学习-朴素贝叶斯（高斯、多项式、伯努利）]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127508481</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127508481</guid><author>qq_45034708</author><pubDate>Thu, 27 Oct 2022 00:11:49 +0800</pubDate><description><![CDATA[贝叶斯分类器主要思想是基于贝叶斯定理，是机器学习中重要的分类算法，适用于高维度的大数据集，速度快，准确率高，一个经典的应用场景是识别垃圾邮件。]]></description><category></category></item><item><title><![CDATA[机器学习-特征提取（one-hot、TF-IDF）]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127455228</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127455228</guid><author>qq_45034708</author><pubDate>Mon, 24 Oct 2022 11:29:52 +0800</pubDate><description><![CDATA[特征工程是机器学习中的第一步，会直接影响机器学习的结果。可以说数据和特征决定了机器学习的上限，而模型和算法只是逼近这个上限。特征工程包括特征提取、特征预处理和特征降维等。]]></description><category></category></item><item><title><![CDATA[机器学习-决策树算法（ID3、C4.5和CART）]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127444049</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127444049</guid><author>qq_45034708</author><pubDate>Sat, 22 Oct 2022 00:15:00 +0800</pubDate><description><![CDATA[决策树（Decision Tree）是⼀种树形结构，每个节点表示⼀个属性上的判断，每个分⽀代表⼀个判断结果的输出，最后每个叶节点代表⼀种分类结果，本质是⼀颗由多个判断节点组成的树。]]></description><category></category></item><item><title><![CDATA[机器学习-支持向量机SVM算法]]></title><link>https://blog.youkuaiyun.com/qq_45034708/article/details/127324747</link><guid>https://blog.youkuaiyun.com/qq_45034708/article/details/127324747</guid><author>qq_45034708</author><pubDate>Mon, 17 Oct 2022 17:29:50 +0800</pubDate><description><![CDATA[支持向量机（Support Vector Machine, SVM）对监督学习下二分类问题提供了一个绝妙的解决方案。通过对偶函数和核函数求解，将适用范围从二维线性推广到多维非线性模型，使用相关方法变形，也可用于多分类问题和回归问题。]]></description><category></category></item></channel></rss>