<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Theodore_1022的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Theodore_1022</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Theodore_1022]]></copyright><item><title><![CDATA[深度学习（15）：倾斜数据集 & 精确率-召回率权衡]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/160403643</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/160403643</guid><author>Theodore_1022</author><pubDate>Wed, 22 Apr 2026 11:51:16 +0800</pubDate><description><![CDATA[在实际机器学习中，我们往往会遇到一个问题：数据严重不均衡（Skewed Dataset）]]></description><category></category></item><item><title><![CDATA[深度学习（14）：项目完整周期 & 公平性伦理]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/160300113</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/160300113</guid><author>Theodore_1022</author><pubDate>Sun, 19 Apr 2026 11:00:26 +0800</pubDate><description><![CDATA[这一篇非常关键，它不再只是“训练模型”，而是带你理解：

一个完整的机器学习项目是如何从0到上线运行的
AI系统的公平性、偏见与伦理问题（工业级必备）]]></description><category></category></item><item><title><![CDATA[深度学习（13）：模型迭代、错误分析与数据策略]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/160299737</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/160299737</guid><author>Theodore_1022</author><pubDate>Sun, 19 Apr 2026 10:49:58 +0800</pubDate><description><![CDATA[在机器学习项目中，模型不是一次就做好的，而是一个不断迭代优化的过程。]]></description><category></category></item><item><title><![CDATA[深度学习（12）正则化线性回归中的偏差与方差调试]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/159432511</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/159432511</guid><author>Theodore_1022</author><pubDate>Tue, 24 Mar 2026 17:53:26 +0800</pubDate><description><![CDATA[高偏差需要提升模型复杂度，高方差需要降低模型复杂度。]]></description><category></category></item><item><title><![CDATA[深度学习（11）：偏差与方差诊断、学习曲线]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/159430207</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/159430207</guid><author>Theodore_1022</author><pubDate>Tue, 24 Mar 2026 16:20:02 +0800</pubDate><description><![CDATA[模型效果不好，到底是哪里出了问题？]]></description><category></category></item><item><title><![CDATA[人工智能（2）知识表示与知识图谱 ]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/154353098</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/154353098</guid><author>Theodore_1022</author><pubDate>Mon, 03 Nov 2025 17:40:33 +0800</pubDate><description><![CDATA[本篇对人工智能知识表示与知识图谱 进行了总结归纳]]></description><category></category></item><item><title><![CDATA[深度学习（10）模型评估、训练与选择]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/154352097</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/154352097</guid><author>Theodore_1022</author><pubDate>Mon, 03 Nov 2025 16:22:37 +0800</pubDate><description><![CDATA[在训练神经网络或机器学习模型时，我们的目标不仅是让模型在训练数据上表现好，更重要的是在未见过的数据上也有良好的表现。这就涉及到模型的评估与验证。]]></description><category></category></item><item><title><![CDATA[人工智能（1）绪论]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/154244981</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/154244981</guid><author>Theodore_1022</author><pubDate>Sun, 02 Nov 2025 09:14:49 +0800</pubDate><description><![CDATA[本篇对人工智能绪论的内容进行了整理，便于复习。]]></description><category></category></item><item><title><![CDATA[深度学习（9）导数与计算图]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/154244711</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/154244711</guid><author>Theodore_1022</author><pubDate>Sat, 01 Nov 2025 17:34:57 +0800</pubDate><description><![CDATA[本篇主要讲解导数与计算图在神经网络中的作用]]></description><category></category></item><item><title><![CDATA[深度学习（8）Adam 优化器、卷积神经网络与反向传播]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/154171685</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/154171685</guid><author>Theodore_1022</author><pubDate>Sat, 01 Nov 2025 17:28:36 +0800</pubDate><description><![CDATA[本文将系统讲解：Adam 优化器、卷积层（CNN），以及反向传播的工作原理。]]></description><category></category></item><item><title><![CDATA[深度学习（7）Softmax回归与多标签分类]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153958513</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153958513</guid><author>Theodore_1022</author><pubDate>Mon, 27 Oct 2025 15:57:56 +0800</pubDate><description><![CDATA[本文将带你从原理、公式、代价函数推导到 TensorFlow 实现，再到多标签分类的区别，完整掌握 Softmax 的核心思想。]]></description><category></category></item><item><title><![CDATA[深度学习（6）激活函数与多类别]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153971557</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153971557</guid><author>Theodore_1022</author><pubDate>Mon, 27 Oct 2025 15:33:50 +0800</pubDate><description><![CDATA[在神经网络（Neural Network）中，激活函数（Activation Function） 是一个非常核心的概念。它决定了每个神经元的输出方式，也直接影响模型的非线性表达能力和训练效果。]]></description><category></category></item><item><title><![CDATA[深度学习（5）一文搞懂向量与矩阵的所有乘法]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153923629</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153923629</guid><author>Theodore_1022</author><pubDate>Sun, 26 Oct 2025 16:52:20 +0800</pubDate><description><![CDATA[本文面向初学者，从最基础的概念出发，系统整理向量与矩阵的各种“乘法”操作。读完这篇文章，你将能彻底分清楚点乘、叉乘、矩阵乘法、元素乘法等操作的区别与用法。]]></description><category></category></item><item><title><![CDATA[神经学习（4）神经网络的向量化实现与TensorFlow训练流程]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153924161</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153924161</guid><author>Theodore_1022</author><pubDate>Sun, 26 Oct 2025 16:51:33 +0800</pubDate><description><![CDATA[本篇对神经网络的向量化实现与TensorFlow训练流程进行了详细介绍。]]></description><category></category></item><item><title><![CDATA[深度学习（3）神经网络]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153869398</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153869398</guid><author>Theodore_1022</author><pubDate>Sat, 25 Oct 2025 17:14:37 +0800</pubDate><description><![CDATA[本篇博客主要对神经网络进行了介绍。]]></description><category></category></item><item><title><![CDATA[深度学习（2）神经元与需求预测]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153867159</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153867159</guid><author>Theodore_1022</author><pubDate>Sat, 25 Oct 2025 10:43:39 +0800</pubDate><description><![CDATA[本篇对深度学习中的神经元和需求预测部分进行了详细介绍，引入了神经学习最开始需要了解的内容。]]></description><category></category></item><item><title><![CDATA[机器学习（10）L1 与 L2 正则化详解]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153829206</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153829206</guid><author>Theodore_1022</author><pubDate>Fri, 24 Oct 2025 15:06:55 +0800</pubDate><description><![CDATA[这篇博客来讲讲最常见的两种正则化：L1 正则化和 L2 正则化]]></description><category></category></item><item><title><![CDATA[机器学习（9）正则化]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153817816</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153817816</guid><author>Theodore_1022</author><pubDate>Fri, 24 Oct 2025 11:00:04 +0800</pubDate><description><![CDATA[本篇对正则化进行了相关介绍。]]></description><category></category></item><item><title><![CDATA[机器学习（8）梯度下降的实现与过拟合问题]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153787793</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153787793</guid><author>Theodore_1022</author><pubDate>Thu, 23 Oct 2025 18:09:18 +0800</pubDate><description><![CDATA[本篇博客主要对过拟合进行了介绍，下一期将会详细讲解过拟合有关的函数。]]></description><category></category></item><item><title><![CDATA[机器学习（7）逻辑回归及其成本函数]]></title><link>https://blog.youkuaiyun.com/Theodore_1022/article/details/153784893</link><guid>https://blog.youkuaiyun.com/Theodore_1022/article/details/153784893</guid><author>Theodore_1022</author><pubDate>Thu, 23 Oct 2025 16:48:23 +0800</pubDate><description><![CDATA[本篇博客对逻辑回归及其成本函数进行了介绍，并且利用肿瘤预测的例子进行了详细讲解。]]></description><category></category></item></channel></rss>