Introduction
1.
Recommender Systems
1.1.
Gradient Boosting
1.2.
TF-IDF
1.3.
Cross Encoders
1.4.
SentenceTransformers
1.5.
Collaborative Filtering
2.
AB Testing
2.1.
Examples
2.2.
Power Analysis
3.
LLMs
3.1.
Fine-tuning
3.2.
Useful Models
3.3.
Encoder vs Decoder
4.
Miscellaneous
4.1.
Bradley-Terry Model
4.2.
Setting up WSL
4.3.
To Read
4.4.
Packages
4.5.
Skills
5.
Identities
5.1.
Sigmoid
5.2.
Statistics
6.
Papers
6.1.
Weinberger 2009 - Hashing for Multitask Learning
6.2.
Burges 2010 - RankNET to LambdaMART
6.3.
Schroff 2015 - FaceNET
6.4.
Schnabel 2016 - Recs as Treatments
6.5.
Guo 2017 - DeepFM
6.6.
Hamilton 2017 - GraphSAGE
6.7.
Ma 2018 - Entire Space Multi-Task Model
6.8.
Reimers 2019 - Sentence-BERT
6.9.
He 2020 - LightGCN
6.10.
Lewis 2020 - Retrieval Augmented Generation
6.11.
Gao 2021 - SimCSE
6.12.
Weng 2021 - Contrastive Representation Learning
6.13.
Dao 2022 - Flash Attention
6.14.
Tunstall 2022 - SetFit
6.15.
Rafailov 2023 - Direct Preference Optimization
6.16.
Blecher 2023 - Nougat
6.17.
Borisyuk 2024 - GNN at LinkedIn
6.18.
Liu 2023 - Meaning Representations from Trajectories
7.
NLP Course
8.
Database Course
8.1.
Lecture 1
8.2.
Lecture 2
8.3.
Lecture 3
Light
Rust
Coal
Navy
Ayu
Chux's Notebook
Summaries of individual papers that I have taken a deeper look into.