Tag: talentme-tech
-
Speculative Decoding: Draft Model Sampling, Rejection Sampling & On-Device Acceleration
> **Core Executive Summary**: Autoregressive decoding generates tokens sequentially. Transferring gigabytes of model weights from GPU HBM for a single token yie
-
Sequence Models Evolution: RNN BPTT, LSTM/GRU Gating, xLSTM Matrix Memory, HiPPO Matrix & Mamba Selective SSM (S6) Guide
> **Summary**: Processing variable-length sequences and capturing long-term dependencies is the core challenge of sequence modeling. This 100% exhaustive guide
-
Reasoning LLMs & Slow-Thinking: DeepSeek-R1 Pure RL, Aha Moment, Long CoT Distillation & OpenAI o1/o3
> **Core Executive Summary**: Traditional intuitive generation models (System 1 / Fast Thinking) rely on pattern matching for one-shot answers, suffering from l
-
Support Vector Machines (SVM): Max-Margin Geometry, Duality, KKT & RBF Kernel Guide
> **Summary**: Support Vector Machine (SVM) is one of the most mathematically elegant algorithms in classical statistical learning. This guide provides a system
-
Multimodal Alignment: CLIP Dual-Tower Contrastive Learning, InfoNCE Loss & SigLIP
> **Core Executive Summary**: Multimodal alignment connects heterogeneous vision, text, and audio data into a shared semantic space. OpenAI’s **CLIP (Contrastiv
-
Offline RL & Imitation Learning: Distribution Shift, BC, CQL, IQL & the Road to RLHF/DPO
> **Core Executive Summary**: Offline Reinforcement Learning (RL) aims to learn a policy from a **fixed, pre-collected dataset** $mathcal{D} = {(s, a, r, s’)
-
TalentMe AI/ML/LLM/Infra 全景技术拓扑图与知识树 (Full AI Knowledge Taxonomy Graph)
> **导读与全景视野**：现代人工智能已经形成由数理基础、深度模型、算力基础设施（AI Infra）、应用工程（AI Engineering）与工业级系统案例组成的庞大体系。本文档作为 TalentMe 前端 Tech Vault 的总纲性技术拓扑图，将 9 大核心子领域的知识图谱全量展开。
-
DS 统计与实验设计：假设检验、功效分析、样本量计算、多重比较、SRM 与 CUPED 全景全解
> **核心摘要**：随机化线上实验是产品因果决策的金标准，DS 面试几乎必考其背后的统计学。本指南完整串起实验全链路：假设设定 (H₀/H₁)、α/β 权衡与功效 power = 1−β、均值与比率两类样本量公式、多重比较校正 (Bonferroni/Holm/FDR-BH)、p-hacking 与 peeking
-
RS 数学推导与白板面经：PPO 剪切损失、DPO 闭式解与 RoPE 旋转矩阵
> **核心摘要**：在算法研究科学家（RS）的白板推导面试（Whiteboard Math Round）中，面试官要求候选人脱离 PPT 和现成工具包，在黑板上从第一性原理推演核心算法的闭式解、最优化边界与概率性质。本指南系统汇编顶会级数学推导：DPO 闭式解与隐式奖励消元、PPO 剪切悲观下界、RoPE 复数内积同
-
Vector DB 向量数据库全景：HNSW 图索引、IVF-PQ 乘积量化与 ANN 相似度检索原理解构
> **核心摘要**：随着 Embedding 模型的普及，高维海量向量检索成为了大模型 RAG 与推荐系统的基石。传统暴力遍历 (FLAT) 检索复杂度为 $O(N cdot D)$，在数十亿规模向量下无法做到毫秒级响应。**向量数据库 (Vector DB)** 通过 **ANN (Approximate Nea