Welcome to the first techletter. I hope to deliver good thoughtful content to you by email. Go to the launch announcement if you want to understand the motivation behind this techletter.
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Summary of useful links
- The Complete Guide to Deep Work by Fadeke Adegbuyi is a very well written actionable guide that derives its inspiration from the book Deep Work: Rules for Focused Success in a Distracted World by Cal Newport. It stresses the importance of intentionally and thoughtfully spending time towards activities in a distraction-free environment that push our cognitive capabilities to the limit. The article lists lots of useful and practical advice and ways to achieve them. For example, it suggests us to start small by starting with just 15 minutes of deep work in a distraction-free environment, and then build on top of that. Furthermore, adding constraints like time pressure is a good strategy to achieve deep work. It then goes on to recommend hard but impactful things like working towards long term goals, eliminating social media from our life, replacing low-quality entertainment like browsing entertainment websites with high-quality entertainment like reading good books. Overall, a very well written and highly useful piece.
- In a similar spirit, “How to do hard things” by David R. MacIver suggests various “systems” for tackling the hard stuff. A single loop system works when the end goal is defined, where you start with an easy thing and move towards the end goal one hard step at a time. A double loop system, on the other hand, works when even the end goal is not defined, and we work towards identifying and understanding the problem better along with working with a single loop system to solve it as well. Finally, David shows us an example of using these approaches to write better. HackerNews has a very good discussion on this topic as well.
Good Free Machine Learning Books
- Mathematics for Machine Learnings
- Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning
- Data Engineering Cookbook
You should already know this, but if not, definitely check these out.
More to come in the next techletter. Happy Reading.
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