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。
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
GPU Hardware Architecture: SM, Tensor Cores, HBM Bandwidth & Roofline Model
> **Core Executive Summary**: AI LLM performance relies directly on underlying GPU hardware physics. Modern GPUs like NVIDIA H100/A100 feature massively paralle
Open & Commercial SOTA LLM Evolution: From BERT/GPT-4 to LLaMA-3, Qwen-3, Gemma-4 & Kimi-K2
> **Core Executive Summary**: Since the Transformer paper, Large Language Models (LLMs) evolved from unidirectional/bidirectional encoders (BERT/GPT-1/2) to lar
Optimization & Matrix Calculus: Lagrange Multipliers, KKT Conditions, SVD & Convergence Geometry
> **Core Executive Summary**: Every machine learning model—from SVM geometric margin maximization to Transformer gradient descent updates—is fundamentally an **
Multimodal Generative System Design: Image/Video Generation & GPU Scaling
> **Core Executive Summary**: Multimodal generative workloads — text-to-image, text-to-video, and vision-language understanding — are the most compute-hungry an
MLE Model Evaluation & Debugging Engineering: CV Strategies, Data Leakage, Bias-Variance Diagnosis, Drift & A/B Validation
> **Core Executive Summary**: A model is only as good as the evaluation loop that validates it. This guide builds the complete evaluation-and-debugging engineer
High-Concurrency AI System Design: SSE Streaming, Semantic Cache & ML Runtimes
> **Core Executive Summary**: Traditional web servers handle millisecond HTTP requests. LLM serving involves multi-second streaming responses. **High-Concurrenc
Mixture-of-Experts (MoE) & DeepSeek MLA/MTP/mHC Architecture: Top-k Routing, Aux-Loss-Free, KAN vs MLP
> **Core Executive Summary**: As model scales reach trillion-parameter frontiers, Dense forward FLOPs become unsustainable. **Mixture-of-Experts (MoE)** replace
AI Math Foundations: Bayes Inference, Shannon Entropy, Cross-Entropy & KL Divergence
> **Core Executive Summary**: Probability theory and information theory form the mathematical backbone of artificial intelligence. From **Bayesian Inference** p