pat
@neuralpat
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ml & math | blog: https://t.co/UaOPBVTAPd
Joined April 2025
if money weren’t a thing, i’d become very learned and a math & physics professor — lecturing students and fueling their curiosity, and helping them to see the world in new ways. wish i can be this version of myself once i’ve provided my family a good life.
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ML GRIND DAY 37:.~3 hr grind. • solved ~90 Qs from ex 4.4 (Exponential & Logarithmic Equations). • watched 2 probability videos on random variables & probability distributions. • read up to pg 30/392 of Introduction to ML with Python. • wrote some basic code.
ML GRIND DAY 36:.~4 hr grind. • watched 2 probability videos on Bayes’ theorem and its examples. • solved ~15 Qs on Leontief’s input-output model. • solved ~70 Qs on logarithmic laws.
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we will be covering a few pages from this book tonight. time to write some code.
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done with this exercise; going to watch a few probability lectures now.
gm folks;.we are going to solve this exercise on exponential and logarithmic equations.
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gm folks;.we are going to solve this exercise on exponential and logarithmic equations.
ML GRIND DAY 36:.~4 hr grind. • watched 2 probability videos on Bayes’ theorem and its examples. • solved ~15 Qs on Leontief’s input-output model. • solved ~70 Qs on logarithmic laws.
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ML GRIND DAY 36:.~4 hr grind. • watched 2 probability videos on Bayes’ theorem and its examples. • solved ~15 Qs on Leontief’s input-output model. • solved ~70 Qs on logarithmic laws.
ML GRIND DAY 35:.~3 hr grind. • watched a probability video on the law of total probability. • solved ~90 Qs on logarithmic functions. • solved ~40 Qs on LU decomposition (A = LU).
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we are going to solve this exercise on logarithmic laws.
going to watch the 2nd probability lecture of the day;.will do calculus after this.
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going to watch the 2nd probability lecture of the day;.will do calculus after this.
solved this exercise on leontief’s input output model. sums are super easy but calculation is tedious.
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solved this exercise on leontief’s input output model. sums are super easy but calculation is tedious.
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ML GRIND DAY 35:.~3 hr grind. • watched a probability video on the law of total probability. • solved ~90 Qs on logarithmic functions. • solved ~40 Qs on LU decomposition (A = LU).
ML GRIND DAY 34:.~3 hr grind. • solved ~90 Qs on exponential functions (ex 4.1). • watched a probability video on conditional probability. • wrote a blog sharing my coding resources for ML. link:
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done with first calculus exercise. going to watch probability lectures now.
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done with first exercise of linear algebra; will cover how is LU decomposition useful in EE tmrw. going to start first calculus exercise now.
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we’re going to pull another all nighter and do as much math as possible.
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ML GRIND DAY 34:.~3 hr grind. • solved ~90 Qs on exponential functions (ex 4.1). • watched a probability video on conditional probability. • wrote a blog sharing my coding resources for ML. link:
ML GRIND DAY 33:.~4.5 hr grind. • watched a probability video on binomial & multinomial coefficients. • solved ch 3 calculus review and test exercise. • solved ~70 Qs on invertible & partitioned matrices (linear algebra).
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gonna grind for the next few hrs. finish this exercise and a few probability lectures.
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new blog out;. it includes all the resources that i'm using to learn programming side of machine learning. check it out here:.
medium.com
How I’m learning the programming side of Machine Learning
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finished writing the blog and almost done with the exercise. off to class now; see y’all in 2 hrs.
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I need hardworking, eager-to-learn people in my feed. If you're one, comment below. Humble request: no shitposters, please! 🙏.
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