31–40 of 58 posts
"The Elements of Statistical Learning" (https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLI...) is far and away the best book I've seen.
It took me hundreds of hours to get through it, but if you're looking to understand things at a pretty deep level, I'd say it's well-worth it.
Even if you stop at chapter 3, you'll still know more than most people, and you'll have a great foundation.
Hope this helps!
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I interview a lot of developers for ML positions at our company. The first red flag is always a lack of math. Candidates who come in with API-level competence ie. can implement an ML algo using this,that or the other API, without any understanding of some basic math behind it, always fare poorly. Atleast in ML, not having an understanding of math is pretty much like claiming expertise in riding a bicycle by watching…
A lack of mathematical intuition is a serious problem for many people from engineering to biology to economics. It certainly plagued me throughout my engineering bachelors studies and is something I continually work to get better at. In my opinion, physics students learn the best framework for thinking and get a very good mathematical intuition. For example, here's a problem from an introductory QM book that really t…
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A lack of mathematical intuition is a serious problem for many people from engineering to biology to economics. It certainly plagued me throughout my engineering bachelors studies and is something I continually work to get better at. In my opinion, physics students learn the best framework for thinking and get a very good mathematical intuition. For example, here's a problem from an introductory QM book that really t…
> A needle of length L is dropped at random onto a sheet of paper ruled with parallel lines a distance L apart. What is the probability that the needle will cross a line? Thickness of line is needed right? Otherwise P approaches 100% as thickness approaches 0?
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I interview a lot of developers for ML positions at our company. The first red flag is always a lack of math. Candidates who come in with API-level competence ie. can implement an ML algo using this,that or the other API, without any understanding of some basic math behind it, always fare poorly. Atleast in ML, not having an understanding of math is pretty much like claiming expertise in riding a bicycle by watching…
A lack of mathematical intuition is a serious problem for many people from engineering to biology to economics. It certainly plagued me throughout my engineering bachelors studies and is something I continually work to get better at. In my opinion, physics students learn the best framework for thinking and get a very good mathematical intuition. For example, here's a problem from an introductory QM book that really t…
The neural networks chapter is tiny (but that's ok - that's not the focus) and some of the questions are really hard - but overall I've really enjoyed it.
I'm a math geek, but I'm also a mostly self-taught data scientist. "The Elements of Statistical Learning" ( https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLI... ) is far and away the best book I've seen. It took me hundreds of hours to get through it, but if you're looking to understand things at a pretty deep level, I'd say it's well-worth it. Even if you stop at chapter 3, you'll still know more than mos…
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Andrew Ng's Coursera ML course is supposed to be pretty accessible. I've also heard good things about Machine Learning for Hackers ( http://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/d... ). Ultimately, ML is a mathematical discipline. You can ask for a gentle approach that gets you to the foot of the mountain, but "if you want to learn about nature, to appreciate nature, it is necessary to understand the la…
I'm currently taking Andrew Ng's Coursera course and I'd agree it's quite accessible. In fact, if you have a solid understanding of calculus and linear algebra, you might find it a bit slow at times.
Earlier quoted context omitted.
I interview a lot of developers for ML positions at our company. The first red flag is always a lack of math. Candidates who come in with API-level competence ie. can implement an ML algo using this,that or the other API, without any understanding of some basic math behind it, always fare poorly. Atleast in ML, not having an understanding of math is pretty much like claiming expertise in riding a bicycle by watching…
A lack of mathematical intuition is a serious problem for many people from engineering to biology to economics. It certainly plagued me throughout my engineering bachelors studies and is something I continually work to get better at. In my opinion, physics students learn the best framework for thinking and get a very good mathematical intuition. For example, here's a problem from an introductory QM book that really t…
A circle of radius L is centered x far away from a border 0How much of the 2pi boundary is outside the zone?
When x -> 0 then it's going to be 50% since one boundary line becomes a tangent and the other goes through the middle. When we move x by k (e.g. f(x+k)) then 2k new points will be added on the left side while 2*k points leave the boundary on the right side. When x=L/2 then the boundary lines will split the circle in four equal parts (since they're tangent to the radius at r/2 on both sides) so intuitively its 50%.
I'm a math geek, but I'm also a mostly self-taught data scientist. "The Elements of Statistical Learning" ( https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLI... ) is far and away the best book I've seen. It took me hundreds of hours to get through it, but if you're looking to understand things at a pretty deep level, I'd say it's well-worth it. Even if you stop at chapter 3, you'll still know more than mos…
ESL pays short shrift to the computational complexity of learning whereas UML explicitly handles both statistical and computational complexity concerns. It doesnt matter how statistically pure your algorithm is if its running time scales exponentially with your data.
All of UML's chapters are conceptually unified even when discussing different ML algorithms, with ESL being more of a grab-bag by chapter.
Still, both high quality and free!