Home - fcrimins/fcrimins.github.io GitHub Wiki
Welcome to the fwiki!
Chronological
- 5 stars: 2020.12.28 Notes on LinkedIn Data Platform with Carl Steinbach
- Idea: 2020.12.21 Spring-based regression with different spring strengths during training vs. prediction/inference
- 5 stars: 2020.12.02 Notes on How to Negotiate Your Job Offer with Prof. Deepak Malhotra
- Idea: Multiple comparisons (false discovery rate) idea (maybe detailed below) from a couple years ago (beginning of 2018?) to handle correlated comparisons
- Idea: Post-publication data
5 stars
- The Grand Unified Theory of Software Architecture - danuker | freedom & tech
- 2020-10-17 Emerging Architectures for Modern Data Infrastructure - A16Z
- 2020-12-23 Vaex: A DataFrame with super strings | by Maarten Breddels | Towards Data Science
- 2020-12-11 Stream All the Things: Architectures for Data that Never Ends
- 2020-10-02 Resilience and Vibrancy: The 2020 Data & AI Landscape – Matt Turck
- 2020-07-29 Scaling Pandas: Dask vs Ray vs Modin vs Vaex vs RAPIDS
4 stars
- 2020-12-16 Data Catalogs Are Dead; Long Live Data Discovery - Monte Carlo Data
- 2020-12-29 Data Mesh Principles and Logical Architecture (Smart Data Library)
2020-06-04
Code Samples
- Simple mean-variance portfolio optimizer (Python): https://github.com/fcrimins/optimizer
- Simple backtesting simulator (Python): https://github.com/fcrimins/simulator
- Hamstoo shared code including data streaming DSL (Scala): https://github.com/Hamstoo/data-model
2018-01-17
During the not so distant future, this information will slowly be migrating to hamstoo.com
- Articles: https://web.kamihq.com/web/viewer.html
- Data Mining (kws: overfitting)
- Data
- Data Science
- Statistics and Statistical Techniques (kws: quant)
- Big Data Architecture (kws: Hadoop, Spark, MongoDB, Cassandra)
- Interview Outline
- Machine Learning Notes
- Notes for Coursera's Fundamentals of Reinforcement Learning course
- Recommender Systems
- Notes for Geoff Hinton's Coursera ML course
- Computer Vision (kws: CNN, OCR)
- AdaBoost Notes
- ML Noted Strengths (kws: principles of ML)
- Machine Knowledge (kws: knowledge graphs)
- NLP Notes
- Game Theory
- Good Programming (kws: education)
- Awesome Open Source Documents
- Tech Hiring
- Agile Notes
- Functional Programming
- Starting a Tech Company (kws: business, entrepreneur)
- On Productivity
- Java Notes
- REST Notes (kws: microservices)
- SQL Notes (kws: relational, database)
- Python Notes
- Go Notes
- Scala Notes
- Haskell Notes
- R Notes
- Git Notes
- Apache Spark
- Linux Notes (kws: UNIX)
- Jupyter Notes (kws: IPython, notebook)
- TensorFlow Notes
- Quantum Thoughts
- Assorted Ideas (kws: random)
- Web Annotation
- Super Spatial Sensers
- Mistakes are how we/everything learns (kws: meme)