Introduction to Recommender Systems: Non-Personalized and Content-Based
Notes from Coursera's Introduction to Recommender Systems: Non-Personalized and Content-Based by Joseph A Konstan and Michael D. Ekstrand.
This is the rendered collections of notes by me, Lex Toumbourou.
You can find the source on the GitHub project.
The notes are collecting using my interpretation of the Zettelkasten method.
More
You can find the source on the GitHub project.
The notes are collecting using my interpretation of the Zettelkasten method.
More
-
-
Data Structures and Performance
Notes from Coursera's Data Structures and Performance by Christine Alvarao, Leo Porter and Mia Minnes.
Contents
-
-
Recursive queries in PostgreSQL
For some data relationships in Postgres (or any other relational database that speaks SQL), recursive queries are near inevitable. Let's say you have some top-level …
-
Mean Absolute Difference - L1 Loss
Mean Absolute Difference (MAE) is a function for assessing Regression predictions. It's also as L1 Loss because it takes the L1 Norm of the error …
-
ML Regression
Notes taken during ML Regression by Coursera.
Linear Algebra Refresher
Matrices and Vectors
- Denoted by
n x m(n rows, m columns).
Special Matrices
- Square …
- Denoted by
-
Root mean-squared error - L2 Loss
Root mean-squared error (RMSE) is a function for assessing Regression predictions. Sometimes called L2 Error because it takes the L2 Norm of the error vector …
-
Machine Learning Foundations: A Case Study Approach
Notes from Coursera's Machine Learning Foundations: A Case Study Approach by Emily Fox and Carlos Guestrin
-
Computational Investing Part I (Coursera)
Coursera Verified Certificates, License GKNAYZY4TH, October 2013
Full course write up available here
Notes
-
