Tag: architecture
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扩散模型全景：DDPM 数理推导、Latent Diffusion (LDM)、DiT 架构与 GPT-4o Native 生成
> **核心摘要**：生成式 AI 的两大支柱分别是自回归模型 (Autoregressive LLMs) 与 **扩散模型 (Diffusion Models)**。扩散模型借鉴了非平衡态热力学 (Non-equilibrium Thermodynamics) 原理，通过向数据添加高斯噪声（前向过程）并学习一步步恢复
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业界经典 System Case Studies：Pinterest 视觉搜索与 Netflix 推荐系统
> **核心摘要**：学习 System Design 的最高境界是研读业界顶级科技巨头的真实架构。本指南全量解构两个经典工业案例——**Pinterest**（视觉搜索与推荐：图像嵌入、PinSage 式图神经网络表征、HNSW 近似最近邻检索、混合检索、多模态表征）与 **Netflix**（流媒体推荐：显式 +
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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
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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
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Writing_Guide
本规范适用于 `content/tech/` 下所有双语主题文件(`*.zh.md` / `*.en.md`)。目标:**面试导向** —— 每个知识点不仅要有公式,还要有”能直接说出口的面试回答”。
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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
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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
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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
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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
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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