Category: AI & 机器学习 (AI & Machine Learning)
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Deep Learning Foundations: Activations Evolution (GELU/SwiGLU), Loss Function Taxonomy (CE/KL/Huber/InfoNCE/ArcFace) & Autograd Backprop Guide
> **Summary**: Non-linear activation functions, loss functions, and backpropagation form the mathematical pillars of deep learning. This exhaustive guide covers
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Preference Alignment: RLHF 3-Stage, PPO Clipped Loss, DPO Math Derivation, GRPO & PRM/ORPO
> **Core Executive Summary**: While Pre-training and Supervised Fine-Tuning (SFT) instill strong language modeling and instruction following in LLMs, models may
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Tokenizer & Decoding Strategies: BPE, WordPiece, SentencePiece, Temperature, Top-k/p, Min-p, Gumbel-Max, Repetition Penalty & Sequence Packing
> **Core Executive Summary**: The text processing pipeline of Large Language Models (LLMs) spans three stages: **Front-end Tokenization**, **Mid-end Autoregress
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Generalization Theory: Inductive Bias, Double Descent & PAC Learning Paradigms
> **Core Executive Summary**: Why do 70B+ parameter LLMs generalize exceptionally without severe overfitting? **Generalization Theory** explains this modern AI
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Diffusion Models: DDPM Derivation, Latent Diffusion, DiT & GPT-4o Native Generation
> **Core Executive Summary**: Generative AI rests on two pillars: Autoregressive LLMs and **Diffusion Models**. Inspired by non-equilibrium thermodynamics, diff
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Industry System Case Studies: Pinterest Visual Search & Netflix Recommendation
> **Core Executive Summary**: Real-world system design mastery comes from studying architectures that actually run at scale. This guide dissects **Pinterest** (
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AIE Agent 生产系统:编排模式、上下文预算、可靠性工程与可观测性
> **核心摘要**：Agent 本质是一个循环——目标、观测、策略、行动、记忆——但生产级 Agent 是一个**带护栏的循环**。本指南覆盖 LLM Agent 上线的完整工程栈：编排模式（ReAct / Plan-and-Execute / Reflexion / 多 Agent）、具备硬终止保证的循环与图式编排
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MLE 算法手写实战：零基础 Pure Numpy 手写 LR、K-Means、Self-Attention 与 NMS
> **核心摘要**：全量拆解 MLE 面试高频机器学习算法手写 (Whiteboard / Live Coding) 实战。深入剖析零依赖 Pure Numpy 实现逻辑回归 (Logistic Regression)、K-Means 迭代聚类、Softmax 数值防溢出技巧与 Self-Attention / NM
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RS 论文拆解与研究 Vision：如何向面试官复述 SOTA 论文创新点
> **核心摘要**：在算法研究科学家（Research Scientist, RS）面试中，面试官最看重的是候选人的**独立科研品味（Research Taste）、前沿技术视野（Research Vision）以及对 SOTA 论文的批判性深度解构能力**。面试绝不是简单复述论文摘要，而是展现“从第一性原理审视问题
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集群调度与 Ray：K8s 调度管线、Raylet 架构、分布式对象存储、弹性扩缩容与 GPU 调度全景
> **核心摘要**：任何 ML 平台都要回答同一个核心问题：给定 $N$ 台节点（CPU/GPU/内存），如何为 $M$ 个任务分配资源，使利用率最高、用户之间公平、且故障不拖垮整个作业？本指南从第一性原理拆解集群调度基本问题（资源分配/优先级/公平性），深入 Kubernetes 调度管线（过滤/打分两阶段、污点/