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Computational Linear Algebra

537 点作者 julianj将近 8 年前

14 条评论

thearn4将近 8 年前
Looks like a reasonable overview of applications of dense linear algebra operations, with very specific applications in mind.<p>I feel like iterative Krylov subspace methods should be around somewhere, but to be honest I&#x27;m not sure what the applications space for linear inverse problems looks like in the deep learning domain. So maybe that wouldn&#x27;t quite fit (despite me finding it to be pretty cool, and these methods sort of eating the lunch of most other inverse methods).<p>I&#x27;d round out the course maybe with a read through of Trefethen-Bau, “Numerical Linear Algebra” for those new to the topic area.
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rabreu08将近 8 年前
Looks like a good course. I think it would benefit if they added some module on implementing some basic Linear system of equations solvers, like gradient or steepest descent. Or even GMRES&#x2F;MINRES or so.. The amout of knowledge that i gained from trying to implement these was remarkable.
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fdej将近 8 年前
&gt; Locality: traditional runtime computations focus on Big O, the number of operations computed. However, for modern computing, moving data around in memory can be very time-consuming<p>I need to nitpick here... Big O notation is a way to describe growth rates of functions. You can count data movements (or anything else) with Big O.
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GregBuchholz将近 8 年前
Anyone have a comparison to the &quot;Code the Matrix&quot; book and lectures?<p><a href="http:&#x2F;&#x2F;codingthematrix.com&#x2F;" rel="nofollow">http:&#x2F;&#x2F;codingthematrix.com&#x2F;</a><p><a href="https:&#x2F;&#x2F;cs.brown.edu&#x2F;video&#x2F;channels&#x2F;coding-matrix-fall-2014&#x2F;" rel="nofollow">https:&#x2F;&#x2F;cs.brown.edu&#x2F;video&#x2F;channels&#x2F;coding-matrix-fall-2014&#x2F;</a>
lemming将近 8 年前
For someone with an ancient undergrad math background and only &quot;interested observer&quot; level of machine learning knowledge, would it be better to do this course before tackling the deep learning one?
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flor1s将近 8 年前
Thanks for sharing this, it seems like a lot of interesting material is being discussed. The audience seems to be more like the hacker news visitor than the average student though, as it feels like little hand holding is provided.<p>I&#x27;ve just started lecture 1 but I already felt some minor frustrations:<p>- One of the links in the first lecture is to a notebook about intro to convolutions but that notebook is just a big code dump.<p>- After executing the exercises, you lose the expected answer. It might be better if the answers were included as a comment in the code fragment.<p>- Sometimes the given answers are not actually the answer but just the computation performed as part of getting the answer. I.e. for the matrix-matrix products section in lecture 1 the suggested answer is just the resulting matrix from doing the matrix product, but according to the question in the text the answer should be the actual cheapest shop.<p>- Is this a USF course or a fast.ai course?<p>I don&#x27;t know if the author is planning on improving the material, because right now it feels a bit like a beta version.
ceyhunkazel将近 8 年前
Top-down approach is the best approach to teach and learn well done!
will_pseudonym将近 8 年前
I&#x27;m hooked by the title and excited by teaching people about the linear algebra gospel. Powering search engines forever.
unityByFreedom将近 8 年前
I&#x27;m excited to do this after I get through as much of the DL course as I can. Maybe that&#x27;s a bit backwards but whatever.<p>Thanks for your hard work, Rachel! Really curious what you two will get up to next.
MarkMMullin将近 8 年前
Nice - I was taken by the &quot;acceptable accuracy&quot; comment, much less stomach acid inducing than &quot;numerical stability&quot; :-)
taw55将近 8 年前
Would one get much out of this course with only minimal ML background?
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kyrre将近 8 年前
Why &#x27;computational&#x27; and not &#x27;numerical&#x27;?
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stuartaxelowen将近 8 年前
... What part of linear algebra isn&#x27;t computational?
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ianai将近 8 年前
I&#x27;ve been wanting to do a math refresher in linear or modern algebra for a while...tempting.