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。
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
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
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
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’)
MLE Coding & Algo Prep: Zero-to-One ML Operators in Pure Numpy
> **核心摘要**：Exhaustive technical deep dive into Pure Numpy handwritten ML operators for live coding interviews.
Cluster Scheduling & Ray: K8s Scheduling Pipeline, Raylet Architecture, Object Store, Autoscaling & GPU Scheduling Full Guide
> **Core Executive Summary**: Every ML platform asks one question: given $N$ nodes of CPU/GPU/memory, how do we place $M$ tasks so resources are utilized, users
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
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
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** (
MLE Core Cheatsheet: High-Frequency Q&A, Competitions & Pinterest
> **Core Executive Summary**: The MLE interview core is a closed loop of recurring topics — **regularization & bias-variance, overfitting diagnosis, feature eng
Distributed Training Parallelism: TP, PP, DP & DeepSpeed ZeRO 1/2/3
> **Core Executive Summary**: Single GPU VRAM cannot host 100B+ LLM training parameters, gradients, and optimizer states (a 70B FP16 model requires 1.12TB train
LLM Hallucination & Factuality: Taxonomies, FActScore, RAGAS, SAFE & Context Extension (PI/NTK/YaRN)
> **Core Executive Summary**: LLMs often generate plausible-sounding but unfactual or logically contradictory text, known as **Hallucination**. Hallucinations r
Linear Algebra Core for AI: Vector Spaces, Four Subspaces, EVD/SVD, Projection & Least Squares, Jacobian/Hessian
> **Core Executive Summary**: Linear algebra is the substrate of machine learning and deep learning: every tensor is a matrix, every layer is a matrix multiplic
Production LLM RAG & Agent System Design: Multi-Tenancy, SSE & High Availability
> **Core Executive Summary**: Moving RAG knowledge bases and agentic systems from demo to enterprise production is a distributed-systems problem as much as an M
MLE Data & Feature Engineering: Quality Pipelines, Categorical Encoding, Imbalance, Selection & Drift Detection
> **Core Executive Summary**: Model performance is decided before training starts — by data quality and feature engineering. This guide covers the full MLE data