ML is cool. Here's a Github repo with a bunch of detailed paper notes, reviews, and summaries.

Most of these were read and reviewed with Paper Club, a group of 5-6 intrepid learners that I started with my friends James Vanneman and Tiger Shen in April 2017 after we had finished all the courses at the Bradfield School of Computer Science. Here's what we've covered so far:
Here are some details about the materials I found most helpful.
Updated: August 4th, 2018

Sources, notes:

Key: ✍️ is a blog post, 📘 is a flashcard, no emoji means notes & highlights.
Rethinking S3: Announcing T4, a team data hub – Quilt October 22nd, 2018
Hardware for Deep Learning. Part 1: Introduction – Intento September 27th, 2018
Efficient and Robust Automated Machine Learning (auto-sklearn paper) September 17th, 2018
✍️ How I learned web development, software engineering, & ML August 4th, 2018
✍️ Learning to be a power user of operating systems July 26th, 2018
High-Skilled White-Collar Work? Machines Can Do That, Too July 8th, 2018
✍️ Machine Learning Engineering in 10 Weeks curriculum v1 July 7th, 2018
Rebooting AI - postulates July 5th, 2018
How not to structure your database-backed web applications: a study of performance bugs in the wild July 4th, 2018