Mind Map of Concepts in ML
Mental map of all the ML learning I'm doing. It's not in any kind of order except categorical. Each document is potentially complete or I'm still taking notes on it. All notes will be updated sporadically.
Recsys
- Foundational Papers in Recommender Systems
- Notes on Multi-Stakeholder Recommender Systems from Recsperts Podcast, January 2022
- Understanding the difference between search and recommendations
- Non-Personalized Recommendations Course from Coursera
Information Retrieval
Machine Learning
- Domain-Specific MultiModal ML with CLIP - September 29, 2022
- Deep dive on the Stable Diffusion Safety Filter
- Understanding ChatGPT
- Small datasets
- Isolation forests
Computer Science
- Lecture on HNSW and Graph Search - September 15,2022
- Algorithms and OOP in Java with a static site generator
- Hash aggregates as a data structure
- Understanding memory allocation in Pandas dataframes
- HyperLogLog and probabilistic counting in streams
Engineering
- "My Philosophy on Alerting from Google SRE"
- Unit Testing in Spark Scala
- Securely Storing Config Creds in Jupyter
- Creating new Python venvs
- Working with PyTorch on AWS
- Pandas Idioms Cheat Sheet
- Stftime and strptime in Python
- Generating fake data for Spark
- PyTorch 2.0 Q&A: Optimizing Transformers for Inference
- Scala Intro Notes
Book Notes
- The Programmer's Brain
- Notes on Learning from Data, Abu-Mostafa
- Machine Learning Design Patterns
- Systems Performance
Conference Notes
All PRs and Typos go here.