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学习大纲
学习大纲
Sticky | Created | Reading Notes
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Interview Experience V2
Interview Experience V2
Created | Machine Learning and Deep Learning
ML/DL/LLM算法面经
EGMN
EGMN
Created | Search, Ads & Reco
视频观看时长预估——EGMN
Ad Bidding
Ad Bidding
Created | Search, Ads & Reco
广告出价算法：从传统式到生成式
Rank Model
Rank Model
Created | Search, Ads & Reco
精排模型——从特征交叉建模到深度兴趣序列建模
LLM —— LLM4Ad&Rec
LLM —— LLM4Ad&Rec
Created | Search, Ads & Reco
LLM在广告推荐领域的应用范式
LLM —— GRs
LLM —— GRs
Created | Search, Ads & Reco
生成式在推荐广告领域的落地
LLM —— RL4LLM
LLM —— RL4LLM
Created | Machine Learning and Deep Learning
RL4LLM：详解LLM对齐技术
LLM —— SFT
LLM —— SFT
Created | Machine Learning and Deep Learning
SFT：从技术原理到工程实战
LLM —— Tokenize
LLM —— Tokenize
Created | Machine Learning and Deep Learning
LLM/VLLM词元化技术介绍
LLM —— Position Embedding
LLM —— Position Embedding
Created | Machine Learning and Deep Learning
为什么LLM的位置编码都使用RoPE？
LLM —— Parameter Calculation
LLM —— Parameter Calculation
Created | Machine Learning and Deep Learning
详解大模型的参数计算与效率分析——以LLaMA为例
LLM —— MoE
LLM —— MoE
Created | Machine Learning and Deep Learning
详解MoE
LLM —— KV Cache
LLM —— KV Cache
Created | Machine Learning and Deep Learning
有哪些KV Cache方法？MLA如何实现KV Cache？
LLM —— Efficient Training
LLM —— Efficient Training
Created | Machine Learning and Deep Learning
LLM中的高效训练技术
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