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@@ -4,10 +4,12 @@
 <time datetime=2026-03-20>2026-03-20</time>
 <span>· 88 min</span></div><h2 class=all-card-title>Probabilistic Machine Learn Advanced Topics Summary</h2><p class=all-card-summary>《Probabilistic Machine Learning: Advanced Topics》第1章“Introduction”详细讲解 1. 引言：从“曲线拟合”到“世界理解” 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 …</p><div class=all-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article><article class=all-card><a class=all-card-link href=/notes/deep-learning-goodfellow/><div class=all-meta><span class=all-section>Notes</span>
 <time datetime=2026-03-16>2026-03-16</time>
-<span>· 48 min</span></div><h2 class=all-card-title>《Deep Learning》Summary</h2><p class=all-card-summary>《深度学习》（Ian Goodfellow 著）第1章“引言”详细讲解 1. 本章概述与定位 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 …</p><div class=all-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article><article class=all-card><a class=all-card-link href=/notes/sutton-rl-v2-summary/><div class=all-meta><span class=all-section>Notes</span>
+<span>· 48 min</span></div><h2 class=all-card-title>《Deep Learning》Summary</h2><p class=all-card-summary>Anki 卡片
+《深度学习》（Ian Goodfellow 著）第1章“引言”详细讲解 1. 本章概述与定位 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 …</p><div class=all-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article><article class=all-card><a class=all-card-link href=/notes/sutton-rl-v2-summary/><div class=all-meta><span class=all-section>Notes</span>
 <time datetime=2026-03-13>2026-03-13</time>
-<span>· 31 min</span></div><h2 class=all-card-title>《Reinforcement Learning An Introduction》Summary</h2><p class=all-card-summary>第一章：强化学习问题（The Reinforcement Learning Problem）详细讲解 1. 引言：从交互中学习 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。
-例如，一个婴儿通过挥动手臂、观察周围，逐渐 …</p><div class=all-card-tags><span>#Book Summary</span><span>#Reinforcement Learning</span></div></a></article><article class=all-card><a class=all-card-link href=/notes/probabilistic-machine-learning-an-introduction-summary/><div class=all-meta><span class=all-section>Notes</span>
+<span>· 31 min</span></div><h2 class=all-card-title>《Reinforcement Learning An Introduction》Summary</h2><p class=all-card-summary>Anki 卡片
+第一章：强化学习问题（The Reinforcement Learning Problem）详细讲解 1. 引言：从交互中学习 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。
+例如，一个婴儿通过挥动手臂 …</p><div class=all-card-tags><span>#Book Summary</span><span>#Reinforcement Learning</span></div></a></article><article class=all-card><a class=all-card-link href=/notes/probabilistic-machine-learning-an-introduction-summary/><div class=all-meta><span class=all-section>Notes</span>
 <time datetime=2026-03-12>2026-03-12</time>
 <span>· 51 min</span></div><h2 class=all-card-title>《Probabilistic Machine Learning: An Introduction》Summary</h2><p class=all-card-summary>Anki 卡片
 第1章“Introduction”详细讲解 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）， …</p><div class=all-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article><article class=all-card><a class=all-card-link href=/notes/prml-summary/><div class=all-meta><span class=all-section>Notes</span>

anki_desks/算法知识点__Deep Learning.apkg

@@ -6,7 +6,8 @@
 <a class="home-action ghost" href=/all/>全部文章</a></div><div class=home-socials><div class=social-icons align=left><a href=https://github.com/QingGo target=_blank rel="noopener noreferrer me" title=Github><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M9 19c-5 1.5-5-2.5-7-3m14 6v-3.87a3.37 3.37.0 00-.94-2.61c3.14-.35 6.44-1.54 6.44-7A5.44 5.44.0 0020 4.77 5.07 5.07.0 0019.91 1S18.73.65 16 2.48a13.38 13.38.0 00-7 0C6.27.65 5.09 1 5.09 1A5.07 5.07.0 005 4.77 5.44 5.44.0 003.5 8.55c0 5.42 3.3 6.61 6.44 7A3.37 3.37.0 009 18.13V22"/></svg>
 </a><a href=/index.xml target=_blank rel="noopener noreferrer me" title=Rss><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M4 11a9 9 0 019 9"/><path d="M4 4a16 16 0 0116 16"/><circle cx="5" cy="19" r="1"/></svg></a></div></div></div><div class=home-hero-panel><div class=home-hero-card><p class=home-card-label>最新文章</p><div class=home-latest><article class=home-card><a class=home-card-link href=/notes/probabilistic-machine-learn-advanced-topics-summary/><div class=home-card-meta><time datetime=2026-03-20>2026-03-20</time>
 <span>· 88 min</span></div><h3 class=home-card-title>Probabilistic Machine Learn Advanced Topics Summary</h3><p class=home-card-summary>《Probabilistic Machine Learning: Advanced Topics》第1章“Introduction”详细讲解 1. 引言：从“曲线拟合”到“世界理 …</p><div class=home-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article><article class=home-card><a class=home-card-link href=/notes/deep-learning-goodfellow/><div class=home-card-meta><time datetime=2026-03-16>2026-03-16</time>
-<span>· 48 min</span></div><h3 class=home-card-title>《Deep Learning》Summary</h3><p class=home-card-summary>《深度学习》（Ian Goodfellow 著）第1章“引言”详细讲解 1. 本章概述与定位 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 …</p><div class=home-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article></div><a class=home-more href=/all/>查看更多 →</a></div></div></div></section></div></main><footer class=footer><span>&copy; 2026 <a href=https://qinggo.github.io/>QingGo的碎碎念</a></span> ·
+<span>· 48 min</span></div><h3 class=home-card-title>《Deep Learning》Summary</h3><p class=home-card-summary>Anki 卡片
+《深度学习》（Ian Goodfellow 著）第1章“引言”详细讲解 1. 本章概述与定位 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 …</p><div class=home-card-tags><span>#Book Summary</span><span>#Machine Learning</span></div></a></article></div><a class=home-more href=/all/>查看更多 →</a></div></div></div></section></div></main><footer class=footer><span>&copy; 2026 <a href=https://qinggo.github.io/>QingGo的碎碎念</a></span> ·
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anki_desks/算法知识点__sutton-RL-v2.apkg

@@ -1,4 +1,4 @@
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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>Fri, 20 Mar 2026 13:24:40 +0800</lastBuildDate><atom:link href="https://qinggo.github.io/index.xml" rel="self" type="application/rss+xml"/><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>&lt;p&gt;&lt;img alt="MLAPP-2-2-mindmap.png" loading="lazy" src="https://qinggo.github.io/images/MLAPP-2-2-mindmap.png"&gt;&lt;/p&gt;
 &lt;h1 id="probabilistic-machine-learning-advanced-topics第1章introduction详细讲解"&gt;《Probabilistic Machine Learning: Advanced Topics》第1章“Introduction”详细讲解&lt;/h1&gt;
 &lt;h2 id="1-引言从曲线拟合到世界理解"&gt;1. 引言：从“曲线拟合”到“世界理解”&lt;/h2&gt;
 &lt;p&gt;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。&lt;/p&gt;
@@ -16,6 +16,7 @@
 &lt;/ul&gt;
 &lt;blockquote&gt;
 &lt;p&gt;&lt;strong&gt;例子&lt;/strong&gt;：一个深度卷积网络可以准确识别图片中的猫和狗，但它不知道“猫为什么长这样”，也不知道如果改变光照或背景，识别结果是否会变。&lt;/p&gt;</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>&lt;p&gt;&lt;img alt="《Deep Learning》思维导图" loading="lazy" src="https://qinggo.github.io/images/deep-learning-goodfellow-mindmap.png"&gt;&lt;/p&gt;
+&lt;p&gt;&lt;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"&gt;Anki 卡片&lt;/a&gt;&lt;/p&gt;
 &lt;h1 id="深度学习ian-goodfellow-著第1章引言详细讲解"&gt;《深度学习》（Ian Goodfellow 著）第1章“引言”详细讲解&lt;/h1&gt;
 &lt;h2 id="1-本章概述与定位"&gt;1. 本章概述与定位&lt;/h2&gt;
 &lt;p&gt;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。&lt;/p&gt;
@@ -60,6 +61,7 @@
 &lt;/li&gt;
 &lt;/ul&gt;
 &lt;p&gt;这个过程形成了一个“深度”的层级结构，因此被称为“深度学习”。&lt;/p&gt;</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>&lt;p&gt;&lt;img alt="&amp;ldquo;Sutton RL V2&amp;rdquo;" loading="lazy" src="https://qinggo.github.io/images/sutton-RL-v2.png"&gt;&lt;/p&gt;
+&lt;p&gt;&lt;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"&gt;Anki 卡片&lt;/a&gt;&lt;/p&gt;
 &lt;h1 id="第一章强化学习问题the-reinforcement-learning-problem详细讲解"&gt;第一章：强化学习问题（The Reinforcement Learning Problem）详细讲解&lt;/h1&gt;
 &lt;h2 id="1-引言从交互中学习"&gt;1. 引言：从交互中学习&lt;/h2&gt;
 &lt;p&gt;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。&lt;br&gt;