<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Seu_Jason的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Seu_Jason</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Seu_Jason]]></copyright><item><title><![CDATA[自注意力机制（Self-Attention Mechanism）的详细介绍]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/142657998</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/142657998</guid><author>Seu_Jason</author><pubDate>Mon, 30 Sep 2024 15:59:40 +0800</pubDate><description><![CDATA[本文介绍了有关自注意力机制相关的信息。]]></description><category></category></item><item><title><![CDATA[深度学习与神经网络中常见的超参数及其解释]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/140620045</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/140620045</guid><author>Seu_Jason</author><pubDate>Mon, 22 Jul 2024 21:21:51 +0800</pubDate><description><![CDATA[在机器学习中，超参数是模型训练过程中需要提前设定的参数，不会通过模型训练自动调整。]]></description><category></category></item><item><title><![CDATA[粒子群PSO优化算法的代码实现（python）]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/140619694</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/140619694</guid><author>Seu_Jason</author><pubDate>Mon, 22 Jul 2024 21:04:03 +0800</pubDate><description><![CDATA[这是我们希望优化的函数。在这个例子中，我们使用的是一个简单的二次函数 f(x)=x2f(x) = x^2f(x)=x2。]]></description><category></category></item><item><title><![CDATA[优化算法——粒子群算法]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/139502839</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/139502839</guid><author>Seu_Jason</author><pubDate>Mon, 22 Jul 2024 20:40:17 +0800</pubDate><description><![CDATA[本文介绍了粒子群算法（PSO）]]></description><category></category></item><item><title><![CDATA[神经网络中的激活函数——Softmax函数]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/139502214</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/139502214</guid><author>Seu_Jason</author><pubDate>Thu, 06 Jun 2024 15:49:41 +0800</pubDate><description><![CDATA[本文介绍了Softmax函数]]></description><category></category></item><item><title><![CDATA[神经网络中的激活函数——Swish函数]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/138999556</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/138999556</guid><author>Seu_Jason</author><pubDate>Fri, 17 May 2024 11:20:39 +0800</pubDate><description><![CDATA[本文介绍了Swish函数]]></description><category></category></item><item><title><![CDATA[神经网络中的激活函数——Sigmoid函数]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/138910165</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/138910165</guid><author>Seu_Jason</author><pubDate>Wed, 15 May 2024 15:30:04 +0800</pubDate><description><![CDATA[本文介绍了Sigmoid函数]]></description><category></category></item><item><title><![CDATA[神经网络中的激活函数——ReLU函数]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/138906388</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/138906388</guid><author>Seu_Jason</author><pubDate>Wed, 15 May 2024 14:02:56 +0800</pubDate><description><![CDATA[本文介绍了ReLUctant函数]]></description><category></category></item><item><title><![CDATA[基于支持向量机的数据分类（libsvm）内含matlab完整版代码]]></title><link>https://blog.youkuaiyun.com/Seu_Jason/article/details/138663045</link><guid>https://blog.youkuaiyun.com/Seu_Jason/article/details/138663045</guid><author>Seu_Jason</author><pubDate>Fri, 10 May 2024 13:59:35 +0800</pubDate><description><![CDATA[支持向量机（Support Vector Machine，SVM）是一种常用于分类和回归分析的监督学习模型。它在机器学习领域中具有广泛的应用，尤其在数据挖掘、模式识别和人工智能领域中得到了广泛的关注和应用。SVM 的核心思想是找到一个最优的超平面来分隔不同类别的数据点。在二维空间中，这个超平面就是一条直线，而在高维空间中，它是一个超平面。最优的超平面是指能够最大化类别间的间隔，使得不同类别的数据点尽可能远离超平面。]]></description><category></category></item></channel></rss>