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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>QingGo的碎碎念</title><link>https://qinggo.github.io/</link><description>Recent content on QingGo的碎碎念</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sun, 29 Mar 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://qinggo.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Architecture Lab</title><link>https://qinggo.github.io/tools/llm-architecture-lab/</link><pubDate>Sun, 29 Mar 2026 00:00:00 +0800</pubDate><guid>https://qinggo.github.io/tools/llm-architecture-lab/</guid><description>一个偏研究笔记风格的模型结构浏览器：先看架构，再看参数摘要和静态统计。首版数据包含公开资料整理的合理占位值，后续可用本地脚本替换为实测结果。</description></item><item><title>线性代数几何直观及其深度学习应用</title><link>https://qinggo.github.io/2026/03/22/matrix-geometry-from-unit-circle-to-deep-learning/</link><pubDate>Sun, 22 Mar 2026 13:00:00 +0800</pubDate><guid>https://qinggo.github.io/2026/03/22/matrix-geometry-from-unit-circle-to-deep-learning/</guid><description>把特征值、奇异值、伪逆、行列式、迹放到同一条几何主线上，用浏览器内的 JSXGraph 交互动画重新理解矩阵。</description></item><item><title>Probabilistic Machine Learn Advanced Topics Summary</title><link>https://qinggo.github.io/notes/probabilistic-machine-learn-advanced-topics-summary/</link><pubDate>Fri, 20 Mar 2026 11:15:01 +0800</pubDate><guid>https://qinggo.github.io/notes/probabilistic-machine-learn-advanced-topics-summary/</guid><description><p><img alt="MLAPP-2-2-mindmap.png" loading="lazy" src="https://qinggo.github.io/images/MLAPP-2-2-mindmap.png"></p>
<p><a href="https://qinggo.github.io/anki_desks/%E7%AE%97%E6%B3%95%E7%9F%A5%E8%AF%86%E7%82%B9__MLAPP2-2.apkg">Anki 卡片</a></p>
<h1 id="probabilistic-machine-learning-advanced-topics第1章introduction详细讲解">《Probabilistic Machine Learning: Advanced Topics》第1章“Introduction”详细讲解</h1>
<h2 id="1-引言从曲线拟合到世界理解">1. 引言：从“曲线拟合”到“世界理解”</h2>
<p>本章作为全书的开篇，旨在说明传统机器学习（尤其是深度学习）的局限性，并勾勒出本书将要探讨的更广阔领域——如何从“模式识别”走向对世界更深刻的理解和建模。</p></description></item><item><title>Base64 Playground</title><link>https://qinggo.github.io/tools/base64-playground/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0800</pubDate><guid>https://qinggo.github.io/tools/base64-playground/</guid><description>一个简单直接的 Base64 编解码工具，适合处理中文、日志片段和临时 payload。</description></item><item><title>Gradient Lab</title><link>https://qinggo.github.io/tools/gradient-lab/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0800</pubDate><guid>https://qinggo.github.io/tools/gradient-lab/</guid><description>一个偏视觉向的小实验，适合快速试出页面背景、按钮或卡片的渐变方案。</description></item><item><title>JSON Formatter</title><link>https://qinggo.github.io/tools/json-formatter/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0800</pubDate><guid>https://qinggo.github.io/tools/json-formatter/</guid><description>粘贴一段 JSON，直接在浏览器里完成格式化、压缩、错误定位和复制。</description></item><item><title>KFC Quiz generator</title><link>https://qinggo.github.io/tools/kfc-quiz-generator/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0800</pubDate><guid>https://qinggo.github.io/tools/kfc-quiz-generator/</guid><description>&lt;strong&gt;K&lt;/strong&gt;eyspace &lt;strong&gt;F&lt;/strong&gt;iltering &lt;strong&gt;C&lt;/strong&gt;hallenge Quiz generator。一个纯前端的小型谜题生成器：给定唯一答案和线索数量，自动搜索线索组合，保证在 A-Z0-9 无重复字符空间里只有一个解。</description></item><item><title>《Deep Learning》Summary</title><link>https://qinggo.github.io/notes/deep-learning-goodfellow/</link><pubDate>Mon, 16 Mar 2026 10:51:34 +0800</pubDate><guid>https://qinggo.github.io/notes/deep-learning-goodfellow/</guid><description><p><img alt="《Deep Learning》思维导图" loading="lazy" src="https://qinggo.github.io/images/deep-learning-goodfellow-mindmap.png"></p>
<p><a href="https://qinggo.github.io/anki_desks/%E7%AE%97%E6%B3%95%E7%9F%A5%E8%AF%86%E7%82%B9__Deep%20Learning.apkg">Anki 卡片</a></p>
<h1 id="深度学习ian-goodfellow-著第1章引言详细讲解">《深度学习》（Ian Goodfellow 著）第1章“引言”详细讲解</h1>
<h2 id="1-本章概述与定位">1. 本章概述与定位</h2>
<p>第1章“引言”是全书的开篇，旨在为读者建立对“深度学习”这一领域的宏观认知。它不是一本技术手册的简单开场白，而是一幅精心绘制的“知识地图”。本章通过回顾人工智能的发展历程、定义核心概念、阐明深度学习的独特优势，为后续所有技术章节奠定了思想和理论基础。</p></description></item><item><title>《Reinforcement Learning An Introduction》Summary</title><link>https://qinggo.github.io/notes/sutton-rl-v2-summary/</link><pubDate>Fri, 13 Mar 2026 16:09:37 +0800</pubDate><guid>https://qinggo.github.io/notes/sutton-rl-v2-summary/</guid><description><p><img alt="&ldquo;Sutton RL V2&rdquo;" loading="lazy" src="https://qinggo.github.io/images/sutton-RL-v2.png"></p>
<p><a href="https://qinggo.github.io/anki_desks/%E7%AE%97%E6%B3%95%E7%9F%A5%E8%AF%86%E7%82%B9__sutton-RL-v2.apkg">Anki 卡片</a></p>
<h1 id="第一章强化学习问题the-reinforcement-learning-problem详细讲解">第一章：强化学习问题（The Reinforcement Learning Problem）详细讲解</h1>
<h2 id="1-引言从交互中学习">1. 引言：从交互中学习</h2>
<p>强化学习的核心思想源于我们日常生活中的一种学习方式：通过与环境的交互，根据结果调整行为，从而达成某种目标。<br>
例如，一个婴儿通过挥动手臂、观察周围，逐渐学会抓握物体；我们学开车时，通过不断尝试和调整，最终能够平稳驾驶。这种“从交互中学习”的模式，正是强化学习研究的起点。</p></description></item><item><title>《Probabilistic Machine Learning: An Introduction》Summary</title><link>https://qinggo.github.io/notes/probabilistic-machine-learning-an-introduction-summary/</link><pubDate>Thu, 12 Mar 2026 11:40:41 +0800</pubDate><guid>https://qinggo.github.io/notes/probabilistic-machine-learning-an-introduction-summary/</guid><description><p><img alt="MLAPP-2-1-mindmap.png" loading="lazy" src="https://qinggo.github.io/images/MLAPP-2-1-mindmap.png"></p>
<p><a href="https://qinggo.github.io/anki_desks/%E7%AE%97%E6%B3%95%E7%9F%A5%E8%AF%86%E7%82%B9__MLAPP2-1.apkg">Anki 卡片</a></p>
<h1 id="第1章introduction详细讲解">第1章“Introduction”详细讲解</h1>
<p>本章是《Probabilistic Machine Learning: An Introduction》的开篇，旨在为读者建立机器学习的基本框架，定义核心概念，介绍三种主要的学习范式（监督学习、无监督学习、强化学习），并讨论数据预处理和常见数据集。本章内容为全书后续章节奠定基础，强调概率视角在机器学习中的核心地位。</p></description></item><item><title>PRML Summary</title><link>https://qinggo.github.io/notes/prml-summary/</link><pubDate>Mon, 09 Mar 2026 13:38:02 +0800</pubDate><guid>https://qinggo.github.io/notes/prml-summary/</guid><description><p><img alt="《PRML》思维导图" loading="lazy" src="https://qinggo.github.io/images/PRML-mindmap.png"></p>
<p><a href="https://qinggo.github.io/anki_desks/%E7%AE%97%E6%B3%95%E7%9F%A5%E8%AF%86%E7%82%B9__PRML.apkg">Anki 卡片</a></p>
<h1 id="模式识别与机器学习prml第1章引言教学讲解">《模式识别与机器学习》（PRML）第1章“引言”教学讲解</h1>
<h2 id="1-本章概述与学习目标">1. 本章概述与学习目标</h2>
<p>第1章是全书的总纲，作者 Christopher M. Bishop 在这一章中系统地介绍了模式识别和机器学习的核心思想、数学工具以及基本框架。本章不涉及复杂的技术细节，而是为后续各章奠定概念基础和提供统一的视角。学习本章后，你应该能够：</p></description></item><item><title>The Book of WHY Summary</title><link>https://qinggo.github.io/notes/the-book-of-why-summary/</link><pubDate>Sat, 28 Feb 2026 11:38:02 +0800</pubDate><guid>https://qinggo.github.io/notes/the-book-of-why-summary/</guid><description><h1 id="the-book-of-why引言mind-over-data深度讲解">《The Book of Why》引言“Mind over Data”深度讲解</h1>
<p><img alt="《The Book of Why》思维导图" loading="lazy" src="https://qinggo.github.io/images/the-book-of-why-mindmap.png"></p>
<p><a href="https://qinggo.github.io/anki_desks/%E7%AE%97%E6%B3%95%E7%9F%A5%E8%AF%86%E7%82%B9__Book%20of%20WHY.apkg">Anki 卡片</a></p></description></item><item><title>What is Devops and SRE</title><link>https://qinggo.github.io/notes/what-is-devops-and-sre/</link><pubDate>Thu, 25 Nov 2021 23:21:36 +0800</pubDate><guid>https://qinggo.github.io/notes/what-is-devops-and-sre/</guid><description>用工程视角解释 DevOps vs SRE：目标、边界与落地方式。</description></item><item><title>以终为始</title><link>https://qinggo.github.io/2021/11/25/%E4%BB%A5%E7%BB%88%E4%B8%BA%E5%A7%8B/</link><pubDate>Thu, 25 Nov 2021 22:16:29 +0800</pubDate><guid>https://qinggo.github.io/2021/11/25/%E4%BB%A5%E7%BB%88%E4%B8%BA%E5%A7%8B/</guid><description>从焦虑与选择出发，梳理目标与路径的思考方法。</description></item><item><title>Hello Hexo</title><link>https://qinggo.github.io/2020/05/10/hello-hexo/</link><pubDate>Sun, 10 May 2020 15:00:14 +0800</pubDate><guid>https://qinggo.github.io/2020/05/10/hello-hexo/</guid><description>记录首次部署 Hexo 博客与 GitHub Pages 的过程。</description></item></channel></rss>