AirSOTA
Air School of Thoughts AtoZAirSOTA 知识矩阵：聚合大模型算法架构、科学育儿情境成长、加州地产考牌实战与全球数字化商业出海的权威专栏。
TalentMe · AI 学习与系统架构
工业级 AI 算法核心 69 题、前沿大模型系统架构演进与北美技术面试全流程备考深度长文。
TalentMe AI/ML/LLM 全景技术拓扑图与知识树 (Full AI Knowledge Taxonomy Graph)
> **导读与全景视野**：现代人工智能与大模型技术已经演进为一个极其庞大、跨学科且数理严密的工程科学体系。从最底层的线性代数与概率卡方分布，到经典机器学习的多项式回归与 SVM，从深度学习的卷积与自注意力机制，到千亿大语言模型 (LLM)、多模态生成、具身智能 Agent 以及 GPU 分布式并行系统。本文档作为 T
DS Statistics & Experiment Design: Hypothesis Testing, Sample Size, Multiple Comparisons, SRM & CUPED
> **Core Executive Summary**: Randomized online experiments are the gold standard for causal product decisions, and DS interviews almost always probe the statis
RS Math Proofs: PPO Clipped Loss, DPO Closed-Form & RoPE Matrix
> **Executive Summary**: Whiteboard math interviews for Research Scientist (RS) positions test your ability to derive closed-form optimal estimators, optimizati
Vector Databases: HNSW Graph Indexing, IVF-PQ Quantization & ANN Similarity Search
> **Core Executive Summary**: High-dimensional vector search powers RAG and recommendation systems. Exact flat search $O(N cdot D)$ fails at scale. **Vector Da
Speculative Decoding: Draft Model Sampling, Rejection Sampling & On-Device Acceleration
> **Core Executive Summary**: Autoregressive decoding generates tokens sequentially. Transferring gigabytes of model weights from GPU HBM for a single token yie
Sequence Models Evolution: RNN BPTT, LSTM/GRU Gating, xLSTM Matrix Memory, HiPPO Matrix & Mamba Selective SSM (S6) Guide
> **Summary**: Processing variable-length sequences and capturing long-term dependencies is the core challenge of sequence modeling. This 100% exhaustive guide
Reasoning LLMs & Slow-Thinking: DeepSeek-R1 Pure RL, Aha Moment, Long CoT Distillation & OpenAI o1/o3
> **Core Executive Summary**: Traditional intuitive generation models (System 1 / Fast Thinking) rely on pattern matching for one-shot answers, suffering from l
Support Vector Machines (SVM): Max-Margin Geometry, Duality, KKT & RBF Kernel Guide
> **Summary**: Support Vector Machine (SVM) is one of the most mathematically elegant algorithms in classical statistical learning. This guide provides a system
Multimodal Alignment: CLIP Dual-Tower Contrastive Learning, InfoNCE Loss & SigLIP
> **Core Executive Summary**: Multimodal alignment connects heterogeneous vision, text, and audio data into a shared semantic space. OpenAI’s **CLIP (Contrastiv
Offline RL & Imitation Learning: Distribution Shift, BC, CQL, IQL & the Road to RLHF/DPO
> **Core Executive Summary**: Offline Reinforcement Learning (RL) aims to learn a policy from a **fixed, pre-collected dataset** $mathcal{D} = {(s, a, r, s’)
TalentMe AI/ML/LLM/Infra 全景技术拓扑图与知识树 (Full AI Knowledge Taxonomy Graph)
> **导读与全景视野**：现代人工智能已经形成由数理基础、深度模型、算力基础设施（AI Infra）、应用工程（AI Engineering）与工业级系统案例组成的庞大体系。本文档作为 TalentMe 前端 Tech Vault 的总纲性技术拓扑图，将 9 大核心子领域的知识图谱全量展开。
DS 统计与实验设计：假设检验、功效分析、样本量计算、多重比较、SRM 与 CUPED 全景全解
> **核心摘要**：随机化线上实验是产品因果决策的金标准，DS 面试几乎必考其背后的统计学。本指南完整串起实验全链路：假设设定 (H₀/H₁)、α/β 权衡与功效 power = 1−β、均值与比率两类样本量公式、多重比较校正 (Bonferroni/Holm/FDR-BH)、p-hacking 与 peeking
RS 数学推导与白板面经：PPO 剪切损失、DPO 闭式解与 RoPE 旋转矩阵
> **核心摘要**：在算法研究科学家（RS）的白板推导面试（Whiteboard Math Round）中，面试官要求候选人脱离 PPT 和现成工具包，在黑板上从第一性原理推演核心算法的闭式解、最优化边界与概率性质。本指南系统汇编顶会级数学推导：DPO 闭式解与隐式奖励消元、PPO 剪切悲观下界、RoPE 复数内积同
Vector DB 向量数据库全景：HNSW 图索引、IVF-PQ 乘积量化与 ANN 相似度检索原理解构
> **核心摘要**：随着 Embedding 模型的普及，高维海量向量检索成为了大模型 RAG 与推荐系统的基石。传统暴力遍历 (FLAT) 检索复杂度为 $O(N cdot D)$，在数十亿规模向量下无法做到毫秒级响应。**向量数据库 (Vector DB)** 通过 **ANN (Approximate Nea
猜想解码 (Speculative Decoding) 全景：Draft Model 小模型草稿、拒绝采样证明与端侧加速
> **核心摘要**：大语言模型在生成阶段逐 Token 自回归预测，每次前向传播都要将数十吉字节的模型权重从 HBM 加载到 GPU 芯片上，算术强度仅为 $O(1)$ FLOPs/Byte（内存带宽极度受限）。**Speculative Decoding (猜想解码)** 引入一个极其轻量的 Draft Model