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Thinking about points β€œfalling” onto lines is simple and easy, but it’s also a helpful first step towards some important dimensionality reduction techniques that can make it easier to build machine le
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Another of my favourite old maths books πŸ“š This one helped me when I was new to probability and statistics, hearing terms like z-score, t-distribution, and ANOVA for the first time. *Disclaimer: Not a
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Apologies to any of my past teachers and lecturers who may have tried to communicate this idea, back when I was too scared of mathematics to be able to appreciate it...!
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In my experience, being able to study maths is less about IQ or natural talent and more about being resourceful. There’s no bigger waste of time than blaming yourself for not understanding a textbook
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Although I’m working through a lot of machine learning maths fundamentals and prerequisites right now, I find it motivating to look ahead at things that seem a little distant now but will soon be with
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I have a particular favourite property of the inner product (but they’re all pretty useful) πŸ“š References and resources: - Deisenroth at al, β€œMathematics for Machine Learning”, 2020 - The course I’m
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I wanted to share this article in case it helps anyone else - even though it makes a simple point (that β€œboring repetition” is what enables mathematical creativity later), it’s something I didn’t real
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I’ve been studying machine learning mathematics around my full-time job for about 8 months now, and this is the routine I’ve settled into. For more practical ML skills, I’m thinking about signing up
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Getting comfortable with abstraction as we define vector spaces, a starting point for applying geometric ideas to more kinds of objects and in higher dimensions πŸ“š References and resources: - CM Bis
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Fears I had when I started studying mathematics for machine learning: 1. How much I’d forgotten since school (no solution to this one except to just start revising topics ASAP!) 2. Exams (I devised s
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It’s much easier to study before work now that the mornings are brighter here in the northern hemisphere πŸŒ…
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Reviewing some fundamentals across probability and random variables today πŸ“š References and resources: - Deisenroth at al, β€œMathematics for Machine Learning”, 2020 - Casella and Berger, β€œStatistical
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About @petal.byte

Louise B is a verified Instagram micro influencer. With 16.4K followers and 34 posts, petal.byte's account shows a follower-to-following ratio of 210.6:1. This profile demonstrates strong community engagement within its niche. View all content anonymously on Pictame - no login required.

petal.byte's engagement rate sits at 4.46%. That's 78% above what you'd typically see for accounts with 10K-100K followers (average 2.5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 58% are videos and 42% are photos. New posts appear roughly every 2 days β€” a steady, reliable rhythm. Captions lean moderate, averaging 318 characters.

4.46% engagement rate β€” 78% above average for 10K-100K follower accounts.
New content every 2 days β€” a steady rhythm.
210.6:1 follower-to-following ratio.

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@petal.byte

Louise B

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πŸ‘©β€πŸ’» Software engineer 𝞹 Studying the mathematics behind machine learning πŸ“š Building @nodepath.app

petalbyte0.github.io
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