Category: AI & 机器学习 (AI & Machine Learning)
-
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
-
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
-
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
-
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
-
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
-
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
-
Industry Recommendation System Design: 3-Stage Pipeline, Two-Tower Models & Feature Store
> **Core Executive Summary**: No single model can score a hundred-million-item corpus within a ~50ms latency SLA. Production systems decompose inference into a
-
AIE 大模型系统设计:千万级 RAG、Code Agent 与推理服务架构
> **核心摘要**:大模型系统设计(LLM System Design)是 AI 应用架构师与 AIE 资深工程师面试的核心考核关卡。与传统分布式系统相比,大模型系统面临四大独特挑战:长上下文吞吐与显存墙瓶颈、非确定性生成的安全沙箱隔离、海量知识库高精度混合召回、以及端到端秒级流式响应。本指南深度拆解企业级多租户 R
-
MLE 模型评估与调试工程:交叉验证策略、数据泄漏、偏差方差诊断、漂移检测与 A/B 验证
> **核心摘要**:模型的价值取决于验证它的评估闭环。本指南完整覆盖 MLE 面试与生产落地中的评估-调试工程链路:如何划分数据并选择正确的交叉验证策略(K-Fold / Stratified / GroupKFold / TimeSeriesSplit / 留一法)、数据泄漏如何静默地虚高每一个离线指标、如何通过