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Joined 1 year ago
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Cake day: August 8th, 2023

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  • Good meme. However I do think that most people starting out will not really have to deal with any of those issues in the first few years apart from maybe the pip/venv/poetry/etc choice. But whatever they’ll pick it’ll probably work well enough for whatever they’re doing. When I started out I didn’t use any external libraries apart from pygame (which probably came pre-installed). I programmed in the IDLE editor that came with Python. I have no idea how I functioned that way, but I learnt a lot and hat plenty of fun.










  • My Thinkpad almost turns 10 this year and I still use it. It’s still quite snappy for normal browsing and programming work on the go. Because I had 2 batteries for it that were easily switchable, the battery that I’m using now is not yet completely dead and will take me 1-2 hours of programming.

    It has had its screen replaced due to someone kicking my bag and breaking the screen, and I’ve had to replace the keyboard at one point after showering the laptop in tea. But the ease with which you can replace stuff like the keyboard is awesome. The thing is definitely built to last


  • gerryflap@feddit.nltomemes@lemmy.worldWhat a time to be "ALIVE"
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    3 months ago

    Okay this is disgusting. It’s only gonna make people’s money problems worse. If you can’t pay for a domino’s pizza out of the pocket, maybe it’s a better idea to go for something cheaper like a freezer pizza from the supermarket. Most people picking this option will be making a mistake.



  • Machine learning and compression have always been closely tied together. It’s trying to learn the “rules” that describe the data rather than memorizing all the data.

    I remember implementing a paper older than me in our “Information Theory” course at university that treated the creation of a decision tree as compression. Their algorithm considered sending the decisions tree and all the exceptions to the decision tree and the tree itself. If a node in the tree increased the overall message size, it would simply be pruned. This way they ensured that you wouldn’t make conclusions while having very little data and would only add the big patterns in the data.

    Fundamentally it is just compression, it’s just a way better method of compression than all the models that we had before.

    EDIT: The paper I’m talking about is “Inferring decision trees using the minimum description length principle” - L. Ross Quinlan & Ronald L. Rivest