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。
AIE Core Cheatsheet: SFT, LoRA, RAG & Agent Interview Map
> **Executive Summary**: The AI / LLM Systems Engineer (AIE) role spans model fine-tuning, retrieval engineering, agentic orchestration, and high-throughput ser
Vision Architectures Evolution: 2D Conv, Receptive Field Calculus, Depthwise Separable Conv, ResNet Identity Mapping & Vision Transformer (ViT) Guide
> **Summary**: Computer vision architectures evolved from handcrafted local inductive biases (CNNs) to data-driven global self-attention (ViT). This 100% exhaus
Transformer Architecture Breakdown: Self-Attention, MHA/GQA/MQA, RoPE & FlashAttention 1/2/3 Operator Fusion
> **Core Executive Summary**: Since its introduction in 2017, the Transformer architecture has fundamentally reshaped artificial intelligence, serving as the un
Vision-Language Models (VLM): ViT, Projectors, LLaVA 2-Stage & DeepSeek-Janus Pro
> **Core Executive Summary**: Vision-Language Models (VLMs) empower LLMs to perceive visual scenes. Rather than training multimodal models from scratch, VLMs ut
AIE Fine-Tuning Guide: Enterprise SFT, LoRA & DPO Alignment
> **Executive Summary**: LLM fine-tuning and alignment empower AI Engineers (AIE) to transform foundation models into domain-specific reasoning engines. In prod
Agent Design Patterns: ReAct Loop, Reflexion Self-Correction, Plan-and-Execute & Graph Engineering
> **Core Executive Summary**: LLMs are evolving from static question-answering engines into autonomous **AI Agents**. Powered by four core pillars—**Brain (LLM)
Deep Learning Debugging & Competition Engineering Taxonomy: 4-Step Debugging Framework, Single Batch Overfitting, Gradient Check & Grad-CAM Guide
> **Summary**: Debugging deep learning models is notoriously challenging because bad code often runs without crashing while silently degrading performance. This
Unsupervised Clustering & KNN: K-Means++, DBSCAN, GMM-EM & KD-Tree Guide
> **Summary**: Clustering and nearest-neighbor methods form the backbone of pattern recognition and representation analysis. This guide explores coordinate desc
World Models & JEPA: Yann LeCun’s Non-Generative Prediction, I-JEPA/V-JEPA & Embodied AI (VLA)
> **Core Executive Summary**: Turing Award winner Yann LeCun proposed **JEPA (Joint Embedding Predictive Architecture)**, advocating abandoning pixel-level reco
AIE LLM System Design Guide: Production RAG, Agent & Serving
> **Executive Summary**: LLM System Design is the central evaluation for AI Application Architects and Senior AI Engineers. Unlike traditional distributed syste
LLM-as-a-Judge Evaluation: Pointwise & Pairwise Paradigms, Bias Elimination & Cohen’s Kappa
> **Core Executive Summary**: Traditional metrics like BLEU and ROUGE fail to evaluate complex semantic quality. **LLM-as-a-Judge** uses strong LLMs (such as GP
Generative Adversarial Networks (GAN) Taxonomy: Minimax Game, JS Divergence Flaw, WGAN Earth Mover Distance & WGAN-GP Guide
> **Summary**: Generative Adversarial Networks (GANs) frame generative modeling as a two-player zero-sum game between a Generator and Discriminator. This 100% e
Decision Trees & Ensemble Methods: CART, GBDT 2nd-Order Taylor & LightGBM Guide
> **Summary**: Tree-based ensemble methods represent the state of the art for tabular datasets. This guide explores decision tree splitting criteria (ID3 / C4.5
Agentic RL & Reasoning Search: MCTS, Process Supervision & RLVR
> **Core Executive Summary**: As LLMs evolve toward **Autonomous Agents** and **System 2 Slow-Thinking**, static single-pass generation gives way to trajectory
DS A/B Testing Case Studies: CUPED, SRM Checks & Attribution
> **Executive Summary**: A/B testing is the gold standard for data-driven product decisions. In real-world enterprise environments, data scientists face three c