Tag: architecture
-
统计推断与假设检验：分布族、极大似然、中心极限定理、p-value、置信区间与功效分析全景
> **核心摘要**：统计推断是将带噪声的数据转化为经过校准的决策的学科，而假设检验是把证据转换为结论的引擎。本指南构建完整链路：常用分布族工具箱（正态、χ²、t、F、泊松、伯努利）；为大样本推断背书的**中心极限定理 (CLT)**；经由**极大似然估计 (MLE)** 与**矩估计**实现点估计并给出无偏性、一致性
-
经典与深度强化学习全景：MDP 体系、Bellman 最优方程、DQN、Policy Gradient、PPO 与 SAC 原理解构
> **核心摘要**：强化学习 (Reinforcement Learning, RL) 是研究智能体 (Agent) 在与动态环境交互过程中，如何通过试错 (Trial-and-Error) 学习最优策略 $pi(a|s)$ 以最大化累积折扣回报的数理科学。从经典表格型 Q-Learning 到结合深度神经网络的
-
搜索与计算广告系统设计:Query 意图理解、分布式倒排索引、RTB 竞价与 pCTR 预估
> **核心摘要**:搜索与广告是同一漏斗的两面:理解用户意图 → 海量候选检索 → 按相关性(广告还需按期望收入)排序 → 在严格延迟预算内混排与投放。搜索侧,本指南覆盖 Query 理解、分布式倒排索引、Recall@K / MRR / NDCG 指标族与 Pointwise/Pairwise/Listwise 学
-
DS Core Cheatsheet: Causal Inference, A/B Testing & Drift
> **Executive Summary**: The fundamental mandate of a Data Scientist (DS) is to drive business growth through rigorous statistical inference, causal identificat
-
RS Experiment Design & Reproducible Research: Ablations, Seed Control, Hyperparameter Search & the Full Reproducibility Checklist
> **Core Executive Summary**: In Research Scientist (RS) interviews — and in research itself — the difference between a credible paper and an irreproducible one
-
Tool Use & Function Calling: Toolformer Self-Taught Calls, JSON Schema & Sandbox Execution
> **Core Executive Summary**: LLMs cannot query real-time APIs or execute code natively. **Tool Use** and **Function Calling** bridge this gap by enabling LLMs
-
AI Safety & Privacy: Prompt Injection, Guardrails, Differential Privacy & Federated Learning
> **Core Executive Summary**: Interactive LLM applications introduce novel security threat vectors. **Prompt Injections** can hijack model behavior, and **PII l
-
Optimizers & Training Engineering Taxonomy: SGD, Momentum, AdamW Decoupled Weight Decay, Xavier/Kaiming Initialization & Gradient Checkpointing Guide
> **Summary**: Optimizers and training engineering bridge the gap between network architecture design and physical GPU memory limits. This 100% exhaustive guide
-
LLM Quantization & Model Compression: INT8/INT4 Mapping, SmoothQuant Outliers, GPTQ Hessian & AWQ/Distillation
> **Core Executive Summary**: As LLM parameter counts scale into tens to hundreds of billions, FP16/BF16 VRAM consumption and memory bandwidth become severe lat
-
Probabilistic Graphical Models: Naive Bayes, HMM Viterbi & Linear-Chain CRF Guide
> **Summary**: Probabilistic Graphical Models (PGM) combine graph theory and probability theory. This guide covers Naive Bayes conditional independence, HMM Vit