<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>HelloAI · 系统化学 AI（学习路径）</title><description>L0 → L7 系统化学习路径 · 数学基础、深度学习、LLM、多模态、对齐、系统工程</description><link>https://ai.xwebgame.com/</link><language>zh-cn</language><item><title>监控与可观测性：LLM 应用的生产管理</title><link>https://ai.xwebgame.com/learn/l7-07-monitoring/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-07-monitoring/</guid><description>一个 LLM 产品上线后，怎么知道它&quot;行不行&quot;？慢了、错了、贵了、被攻击了？这一篇讲监控栈。</description><pubDate>Mon, 31 Aug 2026 00:00:00 GMT</pubDate><category>L7</category><category>监控</category><category>Observability</category><category>LLMOps</category><category>L7</category></item><item><title>AI 安全研究入门：怎么进入这个方向</title><link>https://ai.xwebgame.com/learn/l6-07-safety-research/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-07-safety-research/</guid><description>想做 AI 安全研究？这一篇讲方向、机构、起步项目、推荐阅读——这是 2026 年最稀缺的人才方向之一。</description><pubDate>Sun, 30 Aug 2026 00:00:00 GMT</pubDate><category>L6</category><category>AI 安全</category><category>研究入门</category><category>职业</category><category>L6</category></item><item><title>视频生成：从 Sora 到现代视频 AI</title><link>https://ai.xwebgame.com/learn/l5-06-video-generation/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l5-06-video-generation/</guid><description>Sora、Runway Gen-3、Veo、Kling……2024-2026 视频生成爆发。这一篇讲技术原理 + 工程细节 + 商业格局。</description><pubDate>Sat, 29 Aug 2026 00:00:00 GMT</pubDate><category>L5</category><category>视频生成</category><category>Sora</category><category>Diffusion</category><category>L5</category></item><item><title>LLM 成本优化：从 prompt 到部署的 10× 省钱方法</title><link>https://ai.xwebgame.com/learn/l4-12-cost-optimization/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-12-cost-optimization/</guid><description>LLM 调用又贵又快——一不小心就把 AWS 账单炸了。这一篇讲实战中的成本管控技巧。</description><pubDate>Mon, 24 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>成本优化</category><category>LLM 工程</category><category>L4</category></item><item><title>MCP 工具开发：手把手做一个自己的 MCP server</title><link>https://ai.xwebgame.com/learn/l4-11-mcp-development/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-11-mcp-development/</guid><description>L4-07 讲了 MCP 是什么。这一篇手把手教你写一个能让 Claude / Cursor 直接用的 MCP server。</description><pubDate>Sun, 23 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>MCP</category><category>工程实战</category><category>L4</category></item><item><title>Multi-Agent 系统：让 AI 团队协作</title><link>https://ai.xwebgame.com/learn/l4-10-multi-agent/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-10-multi-agent/</guid><description>一个 Agent 够强了吗？很多场景下不够——需要多个 Agent 分工。AutoGen、CrewAI 等框架开启了&quot;AI 团队&quot;时代。</description><pubDate>Sat, 22 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>Multi-Agent</category><category>AutoGen</category><category>L4</category></item><item><title>训练优化进阶：让大模型训得动</title><link>https://ai.xwebgame.com/learn/l7-06-training-optimization/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-06-training-optimization/</guid><description>梯度检查点 / 混合精度 / Activation Recomputation / ZeRO Offload——这些工程技巧让 70B 模型在单卡上能微调。</description><pubDate>Mon, 17 Aug 2026 00:00:00 GMT</pubDate><category>L7</category><category>训练优化</category><category>Mixed Precision</category><category>L7</category></item><item><title>AI 政策与监管：EU AI Act / 美国 EO / 中国办法对照</title><link>https://ai.xwebgame.com/learn/l6-06-policy/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-06-policy/</guid><description>AI 公司和应用面临的法律义务正在快速演化。这一篇给你全球三大法域的对照表 + 影响。</description><pubDate>Sun, 16 Aug 2026 00:00:00 GMT</pubDate><category>L6</category><category>政策</category><category>监管</category><category>EU AI Act</category><category>AI Safety</category><category>L6</category></item><item><title>TTS 语音合成：从拼接到神经合成</title><link>https://ai.xwebgame.com/learn/l5-05-tts/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l5-05-tts/</guid><description>从机器人腔到逼真人声——TTS 走过的路。VALL-E 让 3 秒声音克隆任何人——这个能力的两面性。</description><pubDate>Sat, 15 Aug 2026 00:00:00 GMT</pubDate><category>L5</category><category>TTS</category><category>语音合成</category><category>L5</category></item><item><title>Tool Use 工程实战：让 LLM 真正会用工具</title><link>https://ai.xwebgame.com/learn/l4-09-tool-use/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-09-tool-use/</guid><description>LLM 能用工具——但要让它&quot;用得稳、用得对、用得省&quot;是另一门工程艺术。这一篇讲实战。</description><pubDate>Mon, 10 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>Tool Use</category><category>Function Calling</category><category>L4</category></item><item><title>LLM 评估：从 MMLU 到真实业务</title><link>https://ai.xwebgame.com/learn/l4-08-llm-evaluation/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-08-llm-evaluation/</guid><description>怎么知道你的 LLM 应用&quot;好不好&quot;？这一篇讲学术 benchmark + 工程评估的完整工具栈。</description><pubDate>Sun, 09 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>评估</category><category>Evaluation</category><category>Benchmark</category><category>L4</category></item><item><title>MCP 协议详解：AI 世界的 USB 接口</title><link>https://ai.xwebgame.com/learn/l4-07-mcp/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-07-mcp/</guid><description>Anthropic 2024 提出的 MCP——让任何工具能接入任何 LLM。一年时间成为 AI Agent 生态的事实标准。</description><pubDate>Sat, 08 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>MCP</category><category>Tool Use</category><category>Agent</category><category>L4</category></item><item><title>模型部署与服务化：从训完到上线</title><link>https://ai.xwebgame.com/learn/l7-05-deployment/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-05-deployment/</guid><description>训出来一个模型只是开始。怎么把它变成一个 24/7 稳定、便宜、可扩展的服务？这一篇讲生产工程。</description><pubDate>Mon, 03 Aug 2026 00:00:00 GMT</pubDate><category>L7</category><category>部署</category><category>MLOps</category><category>推理服务</category><category>L7</category></item><item><title>Whisper：让 AI 听懂 99 种语言</title><link>https://ai.xwebgame.com/learn/l5-04-whisper/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l5-04-whisper/</guid><description>OpenAI 开源的 Whisper 是当下最强语音识别。手机录音转文字、会议纪要、字幕生成——背后几乎都是它。</description><pubDate>Sun, 02 Aug 2026 00:00:00 GMT</pubDate><category>L5</category><category>Whisper</category><category>ASR</category><category>语音识别</category><category>L5</category></item><item><title>In-Context Learning：为什么 LLM 看几个例子就会</title><link>https://ai.xwebgame.com/learn/l4-06-in-context-learning/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-06-in-context-learning/</guid><description>GPT-3 让所有人惊讶的能力——不微调，只给几个例子就能学。它是怎么工作的？这一篇用研究解读。</description><pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate><category>L4</category><category>ICL</category><category>Few-shot</category><category>L4</category></item><item><title>量化深度解析：GPTQ / AWQ / FP8 / GGUF 全谱</title><link>https://ai.xwebgame.com/learn/l7-04-quantization/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-04-quantization/</guid><description>让 70B 模型塞进 24GB 显存——量化是消费级硬件跑大模型的关键。这一篇详解各家方案。</description><pubDate>Mon, 27 Jul 2026 00:00:00 GMT</pubDate><category>L7</category><category>量化</category><category>GPTQ</category><category>AWQ</category><category>FP8</category><category>L7</category></item><item><title>偏见与公平：AI 学到的不止是规则，还有人类的&quot;暗面&quot;</title><link>https://ai.xwebgame.com/learn/l6-05-bias-fairness/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-05-bias-fairness/</guid><description>训练数据是人类社会的镜像——AI 学到的&quot;模式&quot;包含了所有偏见、刻板印象、不公平。这一篇直面这个问题。</description><pubDate>Sun, 26 Jul 2026 00:00:00 GMT</pubDate><category>L6</category><category>偏见</category><category>公平</category><category>Fairness</category><category>L6</category></item><item><title>Agent 构建详解：让 LLM 自己干活</title><link>https://ai.xwebgame.com/learn/l4-04-agent/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-04-agent/</guid><description>从&quot;问一句答一句的对话&quot;到&quot;能连续工作 4 小时的同事&quot;——Agent 是 LLM 应用工程的下一步。</description><pubDate>Sat, 25 Jul 2026 00:00:00 GMT</pubDate><category>L4</category><category>Agent</category><category>Tool Use</category><category>MCP</category><category>L4</category></item><item><title>机制可解释性：看见神经元在想什么</title><link>https://ai.xwebgame.com/learn/l6-04-interpretability/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-04-interpretability/</guid><description>LLM 是黑盒——但研究者已经能从几百亿参数里&quot;读出&quot;具体的概念了。这一篇带你认识 AI 内部的&quot;心理学&quot;研究。</description><pubDate>Mon, 20 Jul 2026 00:00:00 GMT</pubDate><category>L6</category><category>可解释性</category><category>Interpretability</category><category>L6</category></item><item><title>ViT 与 CLIP：让 Transformer 看图</title><link>https://ai.xwebgame.com/learn/l5-03-vit-clip/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l5-03-vit-clip/</guid><description>把图像切成 patch，喂给 Transformer——视觉领域 2020 年最大的范式转变。</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>L5</category><category>ViT</category><category>CLIP</category><category>视觉</category><category>L5</category></item><item><title>Prompt 进阶技巧：CoT / Self-Consistency / Tree of Thoughts / Reflexion</title><link>https://ai.xwebgame.com/learn/l4-02-advanced-prompting/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-02-advanced-prompting/</guid><description>L0-05 教你 10 个基础招。这一篇讲学术研究里的&quot;硬核&quot; prompt 工程——能让 GPT-4 在数学题上从 50% 升到 85%。</description><pubDate>Sat, 18 Jul 2026 00:00:00 GMT</pubDate><category>L4</category><category>Prompt</category><category>CoT</category><category>L4</category></item><item><title>推理优化：vLLM / 量化 / 投机解码 / KV Cache</title><link>https://ai.xwebgame.com/learn/l7-03-inference/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-03-inference/</guid><description>训完模型只是开始。让 LLM 在生产环境跑快、跑省、跑稳，是另一套工程艺术。</description><pubDate>Thu, 16 Jul 2026 00:00:00 GMT</pubDate><category>L7</category><category>推理</category><category>vLLM</category><category>量化</category><category>L7</category></item><item><title>分布式训练：DP / DDP / FSDP / Tensor Parallel 怎么选</title><link>https://ai.xwebgame.com/learn/l7-02-distributed/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-02-distributed/</guid><description>一张 H100 装不下 70B 模型。怎么把训练任务分给几千张卡？这一篇梳理 4 种主流并行策略。</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>L7</category><category>分布式训练</category><category>FSDP</category><category>DDP</category><category>L7</category></item><item><title>GPU 速览：为什么 AI 离不开它</title><link>https://ai.xwebgame.com/learn/l7-01-gpu/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l7-01-gpu/</guid><description>A100、H100、B200 是什么？为啥 AI 用 GPU 不用 CPU？这一篇打开硬件的黑盒。</description><pubDate>Tue, 14 Jul 2026 00:00:00 GMT</pubDate><category>L7</category><category>GPU</category><category>CUDA</category><category>硬件</category><category>L7</category></item><item><title>红队与越狱：攻击 LLM 的方法与防御</title><link>https://ai.xwebgame.com/learn/l6-03-red-team/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-03-red-team/</guid><description>AI 安全的&quot;反派&quot;视角——专门找模型漏洞的人在做什么。理解攻击才能防御。</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>L6</category><category>Red Team</category><category>越狱</category><category>Jailbreak</category><category>L6</category></item><item><title>RLHF 与 Constitutional AI：两大对齐方法对比</title><link>https://ai.xwebgame.com/learn/l6-02-rlhf-cai/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-02-rlhf-cai/</guid><description>OpenAI 用 RLHF，Anthropic 用 CAI。它们都让 LLM &quot;听话&quot;，但思路完全不同。</description><pubDate>Sun, 12 Jul 2026 00:00:00 GMT</pubDate><category>L6</category><category>RLHF</category><category>Constitutional AI</category><category>对齐</category><category>L6</category></item><item><title>为什么需要 AI 对齐：从对错到价值观</title><link>https://ai.xwebgame.com/learn/l6-01-why-alignment/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l6-01-why-alignment/</guid><description>能力越强的 AI，对齐越关键。这一篇讲清楚&quot;对齐&quot;到底是什么，为什么它是 AI 时代最重要的研究方向之一。</description><pubDate>Sat, 11 Jul 2026 00:00:00 GMT</pubDate><category>L6</category><category>对齐</category><category>AI 安全</category><category>L6</category></item><item><title>BERT vs GPT：两大 LLM 流派的本质区别</title><link>https://ai.xwebgame.com/learn/l3-09-bert-vs-gpt/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l3-09-bert-vs-gpt/</guid><description>都是 Transformer，为啥一个做&quot;理解&quot;一个做&quot;生成&quot;？两者的训练目标差一个根本性的设计选择。</description><pubDate>Fri, 10 Jul 2026 00:00:00 GMT</pubDate><category>L3</category><category>BERT</category><category>GPT</category><category>Transformer</category><category>L3</category></item><item><title>SVM 支持向量机：经典 ML 里的几何派</title><link>https://ai.xwebgame.com/learn/l2-08-svm/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l2-08-svm/</guid><description>深度学习火之前，SVM 统治了 2000-2010 整整一个时代。今天它仍然是小数据集和文本分类的最优选之一。</description><pubDate>Thu, 09 Jul 2026 00:00:00 GMT</pubDate><category>L2</category><category>SVM</category><category>分类</category><category>L2</category></item><item><title>Diffusion 数学：从加噪到生成</title><link>https://ai.xwebgame.com/learn/l5-02-diffusion-math/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l5-02-diffusion-math/</guid><description>Stable Diffusion、DALL·E 3、Sora 都基于扩散模型。这一篇讲清楚它的核心数学——用最少的公式。</description><pubDate>Wed, 08 Jul 2026 00:00:00 GMT</pubDate><category>L5</category><category>Diffusion</category><category>Stable Diffusion</category><category>生成模型</category><category>L5</category></item><item><title>多模态总览：AI 如何同时&quot;看、听、读&quot;</title><link>https://ai.xwebgame.com/learn/l5-01-multimodal-overview/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l5-01-multimodal-overview/</guid><description>GPT-4o 能识别你画的草图、Sora 能生成视频——这些&quot;多模态&quot;AI 是怎么做到的？这一篇打开全景图。</description><pubDate>Tue, 07 Jul 2026 00:00:00 GMT</pubDate><category>L5</category><category>多模态</category><category>CLIP</category><category>视觉</category><category>语音</category><category>L5</category></item><item><title>LoRA 微调入门：让大模型&quot;特化&quot;成你需要的样子</title><link>https://ai.xwebgame.com/learn/l4-05-lora/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-05-lora/</guid><description>不用重训整个 70B 模型，只调几百万个参数，一张消费级显卡就能微调 LLM。</description><pubDate>Mon, 06 Jul 2026 00:00:00 GMT</pubDate><category>L4</category><category>LoRA</category><category>微调</category><category>Fine-tuning</category><category>L4</category></item><item><title>RAG 从 0 到 1：让 LLM 基于你的数据回答</title><link>https://ai.xwebgame.com/learn/l4-03-rag/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-03-rag/</guid><description>企业 AI 应用 90% 都是 RAG。这一篇带你搭一个能跑的 RAG 系统——从分块到部署。</description><pubDate>Sun, 05 Jul 2026 00:00:00 GMT</pubDate><category>L4</category><category>RAG</category><category>检索</category><category>LLM</category><category>工程实战</category><category>L4</category></item><item><title>LLM 是怎么炼成的：Pretrain → SFT → RLHF 全流程</title><link>https://ai.xwebgame.com/learn/l4-01-llm-training/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l4-01-llm-training/</guid><description>&quot;训练一个 ChatGPT&quot; 不是一步，是三步。每一步用完全不同的数据和目标。看完你能跟工程师对话。</description><pubDate>Sat, 04 Jul 2026 00:00:00 GMT</pubDate><category>L4</category><category>LLM</category><category>Pretrain</category><category>RLHF</category><category>L4</category></item><item><title>完整 Transformer 架构：把所有积木组合起来</title><link>https://ai.xwebgame.com/learn/l3-08-transformer-architecture/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l3-08-transformer-architecture/</guid><description>注意力是核心，但 Transformer 还有位置编码、FFN、残差连接、LayerNorm。这一篇把它们组合成一个完整可跑的模型。</description><pubDate>Fri, 03 Jul 2026 00:00:00 GMT</pubDate><category>L3</category><category>Transformer</category><category>BERT</category><category>GPT</category><category>L3</category></item><item><title>RNN / LSTM 兴衰：序列模型简史</title><link>https://ai.xwebgame.com/learn/l3-04-rnn/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l3-04-rnn/</guid><description>2017 年 Transformer 出现前，所有处理语言/语音的 AI 都靠 RNN 和它的衍生品。理解它的兴衰，才理解为什么 attention 是革命。</description><pubDate>Thu, 02 Jul 2026 00:00:00 GMT</pubDate><category>L3</category><category>RNN</category><category>LSTM</category><category>序列建模</category><category>L3</category></item><item><title>从感知机到多层神经网络：深度学习的起点</title><link>https://ai.xwebgame.com/learn/l3-01-perceptron-to-mlp/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l3-01-perceptron-to-mlp/</guid><description>所有神经网络都是从一个 1958 年的简单模型扩展来的。这一篇讲清楚&quot;神经元&quot;到底是个啥。</description><pubDate>Wed, 01 Jul 2026 00:00:00 GMT</pubDate><category>L3</category><category>感知机</category><category>神经网络</category><category>激活函数</category><category>L3</category></item><item><title>评估指标 + 过拟合 + 正则化：让模型不犯傻的工程实战</title><link>https://ai.xwebgame.com/learn/l2-07-evaluation/</link><guid 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isPermaLink="true">https://ai.xwebgame.com/learn/l2-05-random-forest/</guid><description>单棵树平庸，一群树投票就能逆天。Kaggle 十年霸主 XGBoost 的来历，就在这一篇。</description><pubDate>Sun, 28 Jun 2026 00:00:00 GMT</pubDate><category>L2</category><category>随机森林</category><category>XGBoost</category><category>集成学习</category><category>L2</category></item><item><title>决策树：最直观可解释的 ML 算法</title><link>https://ai.xwebgame.com/learn/l2-04-decision-trees/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l2-04-decision-trees/</guid><description>一连串&quot;是不是&quot;问题，组成一棵决策的树。它是 XGBoost、LightGBM 等竞赛王者算法的基石。</description><pubDate>Sat, 27 Jun 2026 00:00:00 GMT</pubDate><category>L2</category><category>决策树</category><category>分类</category><category>L2</category><category>可解释 ML</category></item><item><title>逻辑回归与分类：从回归到决策</title><link>https://ai.xwebgame.com/learn/l2-03-logistic-regression/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l2-03-logistic-regression/</guid><description>名字叫&quot;回归&quot;其实是分类。在神经网络出来之前，逻辑回归是工业界分类的事实标准——今天仍然是 baseline。</description><pubDate>Fri, 26 Jun 2026 00:00:00 GMT</pubDate><category>L2</category><category>逻辑回归</category><category>分类</category><category>L2</category><category>Sigmoid</category></item><item><title>线性回归：最简单也最深刻的 ML 模型</title><link>https://ai.xwebgame.com/learn/l2-02-linear-regression/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l2-02-linear-regression/</guid><description>你以为线性回归很简单？神经网络的最后一层、几乎所有 ML 的起点——都是它。</description><pubDate>Thu, 25 Jun 2026 00:00:00 GMT</pubDate><category>L2</category><category>线性回归</category><category>监督学习</category><category>L2</category></item><item><title>监督 / 无监督 / 强化：机器学习的三大世界观</title><link>https://ai.xwebgame.com/learn/l2-01-ml-paradigms/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l2-01-ml-paradigms/</guid><description>所有机器学习算法都属于这三类之一。理解这个分类，你立刻能给任何算法&quot;找它的家&quot;。</description><pubDate>Wed, 24 Jun 2026 00:00:00 GMT</pubDate><category>L2</category><category>ML</category><category>监督学习</category><category>无监督学习</category><category>强化学习</category><category>L2</category></item><item><title>PyTorch 基础：张量、自动求导、你的第一个神经网络</title><link>https://ai.xwebgame.com/learn/l1-10-pytorch/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l1-10-pytorch/</guid><description>把 L1 全部的数学和 Python 武器组合起来——这是你的&quot;AI 工程师&quot;入门仪式。</description><pubDate>Tue, 23 Jun 2026 00:00:00 GMT</pubDate><category>L1</category><category>PyTorch</category><category>神经网络</category><category>L1</category><category>深度学习</category></item><item><title>Pandas 数据处理：DataFrame 是 ML 数据的标准载体</title><link>https://ai.xwebgame.com/learn/l1-09-pandas/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l1-09-pandas/</guid><description>看到 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GMT</pubDate><category>L3</category><category>CNN</category><category>卷积</category><category>视觉</category><category>L3</category></item><item><title>注意力机制详解：从直觉到完整推导</title><link>https://ai.xwebgame.com/learn/l3-05-attention/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l3-05-attention/</guid><description>Attention is all you need. 这一篇带你从&quot;它到底在干嘛&quot;到&quot;它的每行公式&quot;，一次性吃透 Transformer 的核心。</description><pubDate>Mon, 15 Jun 2026 00:00:00 GMT</pubDate><category>L3</category><category>Transformer</category><category>Attention</category><category>Q-K-V</category><category>L3</category><category>深度学习</category></item><item><title>优化器深度解析：SGD / Momentum / Adam 为什么是这样</title><link>https://ai.xwebgame.com/learn/l2-09-optimizers/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l2-09-optimizers/</guid><description>你以为梯度下降只是&quot;沿梯度反方向走&quot;——其实有 10 多种花式走法。Adam 凭什么成为深度学习默认款？</description><pubDate>Sun, 14 Jun 2026 00:00:00 GMT</pubDate><category>L2</category><category>优化器</category><category>SGD</category><category>Adam</category><category>L2</category></item><item><title>概率与最大似然：为什么 ML 是个概率问题</title><link>https://ai.xwebgame.com/learn/l1-04-probability/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l1-04-probability/</guid><description>所有 ML 模型其实都在干同一件事：寻找让&quot;看到这些数据&quot;的概率最大的参数。这个观点一旦你 get 了，整个 ML 都通透了。</description><pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate><category>L1</category><category>概率</category><category>最大似然</category><category>L1</category><category>统计</category></item><item><title>导数与梯度：&quot;学习&quot;的数学定义</title><link>https://ai.xwebgame.com/learn/l1-03-derivatives/</link><guid isPermaLink="true">https://ai.xwebgame.com/learn/l1-03-derivatives/</guid><description>&quot;训练神经网络&quot; 的本质就是导数。把这个词搞懂，AI 的训练流程在你眼里就再不神秘了。</description><pubDate>Fri, 12 Jun 2026 00:00:00 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