Learning Days

Most of my workweek tends to quickly fill up with sporadic founder meetings, company diligence, and portfolio company support. Rarely can I find the luxury of uninterrupted work blocks to sit for hours on end and properly study anything I want to learn.

In light of this year’s theme around deep work, I’ve been experimenting with the concept of “Learning Days” where I pick any topic I’m interested in and spend a full day (Saturdays or Sundays) deep-diving into that topic.

My first two learning days (Artificial Intelligence/Machine Learning & Blockchain) were relatively unstructured as I’m still figuring out the best approach to these going forward.

So far, I’ve kicked off each learning day in the following manner:

  1. Wake up and head to the gym for a run (a healthy body is a healthy mind)
  2. Shower and make tea
  3. Create a new Google Doc and throw it up on my second external monitor to document the day’s notes / learnings
  4. Look for a Medium article where someone has already aggregated the best links / resources for learning the topic of choice. For example, my buddy Sam DeBrule compiled a massive list of curated AI / ML links which drastically helped to cut down the time I needed to spend digging around the web.(obviously you’ll want to do your own research but these types of aggregated resources provide a launching point for mapping out what you know / don’t know)
  5. Go through each article I find: read and reflect. Each article should provide foundational knowledge for the next but I try to question / probe everything I’m reading. I want to develop a sense of which authors know what they’re talking about vs. those that are regurgitating information / filling my head with incorrect information.
  6. In my first hour, I try to first understand the history behind the topic (i.e. when and why did AI / ML first come into existence, what worked and what failed, what catalysts are leading to breakthroughs now)
  7. Now that I have better context around how we got to this point, I’m better able to understand the current state of AI / ML as well as map out a hierarchy of subtopics (i.e. AI -> Machine Learning -> Deep Learning -> Neural Networks) that I’ll need to prioritize my learning around for the next few hours
  8. For each subtopic, I try to understand: what it is (literally, what is deep learning), how it works (deep learning uses neural networks? ok, how do neural networks work?), and how it’s used in practical business / life (Google Photos uses deep learning for face recognition? much wow). I was never a great academic and this last part helps me tie concepts I’m learning to the “real world” so I don’t forget them.
  9. I’m also careful to include my source links next to all of my Google Doc notes so I can easily reference them at a later time.
  10. At the end of the day, I’ll revisit all of the raw notes I’ve taken and take some time to organize / structure them in a readable format – the hope is that someone completely foreign to the topic could pick up my notes and easily understand what I’ve learned that day
  11. As an additional forcing function, I’ve tried to “Feynman Technique” the whole process by teaching my significant other everything I’ve learned. Teaching the subject matter helps me quickly figure out which concepts or subtopics I need to spend more time on. It also helps that she likes to play the “why why why” question game which really forces me to simplify each complex concept. After teaching the topic, I have a list of follow-up questions for myself that I need to dig into since I couldn’t easily explain them.

I’ve got a long ways to go with these Learning Days but I’m loving them and they’ve spurred a new love of learning that makes me even more excited to wake up every morning.

 

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