- Introduction
- 1. Current Focus
2. Recommender Systems
- 2.1. Recent Trends
- 2.2. Gradient Boosting
- 2.3. TF-IDF
- 2.4. Cross Encoders
- 2.5. SentenceTransformers
- 2.6. Collaborative Filtering
- 2.7. Evaluation
- 3. AB Testing
- 3.1. Examples
- 3.2. Power Analysis
- 4. LLMs
- 4.1. Auto Prompt Optimization
- 4.2. Fine-tuning
- 4.3. Useful Models
- 4.4. Encoder vs Decoder
- 4.5. Contextualized Recommendations
- 5. Statistics
- 5.1. Conformal Predictions
- 6. Miscellaneous
- 6.1. Bradley-Terry Model
- 6.2. Setting up WSL
- 6.3. To Read
- 6.4. Packages
- 6.5. Skills
- 6.6. Hash Collisions
- 7. Identities
- 7.1. Sigmoid
- 7.2. Statistics
- 8. Papers
- 8.1. Weinberger 2009 - Hashing for Multitask Learning
- 8.2. Rendle 2009 - Bayesian Personalized Ranking
- 8.3. Burges 2010 - RankNET to LambdaMART
- 8.4. Schroff 2015 - FaceNET
- 8.5. Covington 2016 - Deep NNs for Youtube Recs
- 8.6. Schnabel 2016 - Recs as Treatments
- 8.7. Bateni 2017 - Affinity Clustering
- 8.8. Guo 2017 - DeepFM
- 8.9. Hamilton 2017 - GraphSAGE
- 8.10. Ma 2018 - Entire Space Multi-Task Model
- 8.11. Kang 2018 - SASRec
- 8.12. Reimers 2019 - Sentence-BERT
- 8.13. Yi 2019 - LogQ Correction for In Batch Sampling
- 8.14. Zhao 2019 - Recommending What to Watch Next
- 8.15. Lee 2020 - Large Scale Video Representation Learning
- 8.16. He 2020 - LightGCN
- 8.17. Lewis 2020 - Retrieval Augmented Generation
- 8.18. Gao 2021 - GradCache
- 8.19. Gao 2021 - SimCSE
- 8.20. Weng 2021 - Contrastive Representation Learning
- 8.21. Li 2021 - TaoBao Embedding-Based Retrieval
- 8.22. Zou 2021 - PLM Based Ranking in Baidu Search
- 8.23. Dao 2022 - Flash Attention
- 8.24. Wei 2022 - CoT Prompting in LLMs
- 8.25. Honovich 2022 - Instruction Induction
- 8.26. Huang 2022 - LLMs can Self Improve
- 8.27. Tunstall 2022 - SetFit
- 8.28. Wang 2022 - Self Consistency LLM
- 8.29. Rafailov 2023 - Direct Preference Optimization
- 8.30. Blecher 2023 - Nougat
- 8.31. Dong 2023 - MINE Loss
- 8.32. Liu 2023 - Meaning Representations from Trajectories
- 8.33. Klenitskiy 2023 - BERT4Rec vs SASRec
- 8.34. Singh 2023 - Semantic IDs for Recs
- 8.35. Yang 2023 - OPRO
- 8.36. Rajput 2023 - Generative Retrieval
- 8.37. Borisyuk 2024 - GNN at LinkedIn
- 8.38. Wang 2024 - LLM for Pinterest Search
- 8.39. Solatorio 2024 - GISTEmbed
- 8.40. Sanjabi 2025 - 360Brew
- 8.41. Zhang 2025 - Qwen3 Embedding
9. Talks
- 9.1. Yan 2025 - LLM for Recsys
- 9.2. Tandon 2025 - Gemini for YouTube
- 9.3. Hameed 2025 - 360Brew
- 10. NLP Course
- 11. Database Course
- 11.1. Lecture 1
- 11.2. Lecture 2
- 11.3. Lecture 3
- 12. RL Course
- 12.1. Introduction
- 12.2. MDPs
- 12.3. Dynamic Programming
- 12.4. Model Free Prediction
- 12.5. Model Free Control
- 12.6. Value Function Approximation
- 12.7. Policy Gradient