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#learning

41 posts23 participants0 posts today

Which strategy do you use when learning something new?

3 strategies AI agents use to learn what works:
:blobcoffee: Greedy: Stick with what has worked best so far.
:blobcoffee: ε-Greedy: Mostly stick with the best. But try something new every now and then.
:blobcoffee: Optimistic Start: Assume everything is great until proven otherwise.

They all come from something called the “Multi-Armed Bandit” problem.

But they show up in real life too:
→ Trying a new café.
→ Deciding what to study
→ Choosing which project to pursue at work.

Which one do you use most often?
And should you change it?

Curious to dive deeper? I covered both topics in my latest two articles: towardsdatascience.com/author/

Towards Data ScienceSarah Schürch, Author at Towards Data ScienceRead articles from Sarah Schürch on Towards Data Science.

Finally got my new home router/firewall: TLSense N100L4: 4x 2.5Gbit LAN, N100 CPU, 256GB NVMe SSD, 16GB RAM ( from teklager.se h/t to @meriksson )

This bad boy has already caused me to rethink my life choices and whether or not I'm sane. They who said firewall rules, NATs and such is fun and games should... be sent to the principals office.

But... having now spent a significant amount of time configuring I'm starting to get things into a state that I'm happy with. Multi-WLAN, WireGuard based interfaces, selective routing and all those other fun things.

And I still have absolutely no clue about what reflective NAT is, or whatever all those 513 other settings do... but perhaps it's like DSLR cameras... you mostly use auto and convince yourself that you'll learn that other stuff eventually...

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