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:
- summer '16: organized a coworkers' book club for Introduction to Statistical Learning in R
- spring-summer '17: fast.ai part 1 & 2
- fall '17: reading papers and writing blog posts on our Medium publication
- winter '17: NIPS '17 field trip, implementing papers
- spring '18: diving deep on Bayesian methods
- summer-fall '18: working through a course I've organized called Machine Learning Engineering in 10 Weeks, which is based on what I've found useful at Sourceress.
Here are some details about the materials I found most helpful.