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Gilbert Strang's New Course on Linear Algebra+ for ML Now Online

161 points by xamdam 7y ago

OCW link: https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/video-lectures/ YouTube playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k Book link: https://amzn.to/2WecEkk

xamdam 7y ago
Oh-oh, no linkifying in text submissions, well here we go: OCW link: https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/video-lectures/ https://ocw.mit.edu/courses/mathematics/18-065-matrix-method... YouTube playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIU... Book link: https://amzn.to/2WecEkk https://amzn.to/2WecEkk
netwanderer3 7y ago
I love how the introductory video gives you a big picture of different ways the math can be applied to real life scenarios. I wish they did this more frequently at school. Often the students did not understand at the beginning how all these stuff could be useful and thus they did not invest in the effort or were not interested at all. Each curriculum at school should have an introductory course that clearly outlines and explains how the content of each course can be applied to real life scenarios, including examples of use cases that help students understand the big picture and to motivate them.
- localhost 7y ago
  +1. Motivation is perhaps the most important aspect of teaching. I find that lectures are a terrible medium for teaching a concept; I find that I need time for quiet contemplation / practice / experimentation to truly understand something. However, lectures are fantastic for motivation and encouraging me to do the extra work on my own. I've been reading (along with my kids) Ben Orlin's Math with Bad Drawings book which is great at motivating and presenting math in interesting ways that are all about presenting things as an interesting puzzle to be solved vs. the way much of math is presented in a rote "this is the algorithm / solution" way. https://books.google.com/books/about/Math_with_Bad_Drawings.html?id=2G9BDwAAQBAJ&printsec=frontcover&source=kp_read_button#v=onepage&q&f=false https://books.google.com/books/about/Math_with_Bad_Drawings....
melling 7y ago
I skipped to the end and watched Alan Edelman’s talk on Julia. https://youtu.be/rZS2LGiurKY https://youtu.be/rZS2LGiurKY Alan (co-author of Julia) mentions that only Swift and Julia make the cut for ML, according to Google. I do like Swift and I’m willing to learn Julia, but most of the ML I see uses Python. Is there any traction for Julia or Swift, or is it mostly aspirational at the moment?
- saboot 7y ago
  Can anyone comment on how well developed the CUDA support is on Julia? Compared to Python? I'm using Python/Numba for my scripts.
  - idiliv 7y ago
    From my experience (and this seems to be a general trend with Julia projects) CUDA support in Julia - is not completely stable - is very fast moving - has a low learning curve - is highly amenable to your own tweaks and hacks
- xamdam 7y ago
  Curious what your source for "make the cut" is. I have my own interpretation, but would like to see the original.
  - nexus7 7y ago
    As written above, the cut is this evaluation -- https://github.com/tensorflow/swift/commit/028f245ef3ca735b18379e7340e08a52a787bd5b#diff-bb05f2ec01e7a0e33b23298eabf44dc5 https://github.com/tensorflow/swift/commit/028f245ef3ca735b1... ... written by Chris Lattner when he joined Google Brain and found Python lacking as a language for doing ML work. He evaluated a bunch of alternatives and decided that Swift would be a good choice, so he's now working with a team to explore that. As far as Google at large is concerned, there was no cut of anything. Google keeps producing new ML code in Python and new ML frameworks in Python on a daily basis
- anonymousJim12 7y ago
konz 7y ago
Previous discussion: https://news.ycombinator.com/item?id=19933274 https://news.ycombinator.com/item?id=19933274
- xamdam 7y ago
  Yeah, just noticed. Posted this (even earlier) on Reddit ML it didn't get as much interest
SantalBlush 7y ago
Love Gilbert Strang's original linear algebra lectures on YouTube [1]. They were a huge help while I took the course. In my opinion, he takes great care to motivate each topic and express his train of thought when working through problems. [1] https://m.youtube.com/watch?v=ZK3O402wf1c https://m.youtube.com/watch?v=ZK3O402wf1c
dsiegel2275 7y ago
Strang is an excellent lecturer - his videos for 18.06 (Linear Algebra) were instrumental for me in relearning the subject matter more than twenty years after I last studied it. I used 18.06 to learn the linear algebra necessary to prepare for further study in machine learning. It was sufficient for that purpose, (I got a B and an A in CMU's 10-601 and 10-605 ML courses, respectively) but this 18.065 course is more specifically geared for that purpose. The companion book is quite good, as well.
- liopleurodon 7y ago
  I second this. His videos are phenomenal

161 points by xamdam 7y ago

xamdam 7y ago

Oh-oh, no linkifying in text submissions, well here we go: OCW link: https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/video-lectures/ https://ocw.mit.edu/courses/mathematics/18-065-matrix-method... YouTube playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIU... Book link: https://amzn.to/2WecEkk https://amzn.to/2WecEkk

netwanderer3 7y ago

I love how the introductory video gives you a big picture of different ways the math can be applied to real life scenarios. I wish they did this more frequently at school. Often the students did not understand at the beginning how all these stuff could be useful and thus they did not invest in the effort or were not interested at all. Each curriculum at school should have an introductory course that clearly outlines and explains how the content of each course can be applied to real life scenarios, including examples of use cases that help students understand the big picture and to motivate them.

localhost 7y ago
+1. Motivation is perhaps the most important aspect of teaching. I find that lectures are a terrible medium for teaching a concept; I find that I need time for quiet contemplation / practice / experimentation to truly understand something. However, lectures are fantastic for motivation and encouraging me to do the extra work on my own. I've been reading (along with my kids) Ben Orlin's Math with Bad Drawings book which is great at motivating and presenting math in interesting ways that are all about presenting things as an interesting puzzle to be solved vs. the way much of math is presented in a rote "this is the algorithm / solution" way. https://books.google.com/books/about/Math_with_Bad_Drawings.html?id=2G9BDwAAQBAJ&printsec=frontcover&source=kp_read_button#v=onepage&q&f=false https://books.google.com/books/about/Math_with_Bad_Drawings....

melling 7y ago

I skipped to the end and watched Alan Edelman’s talk on Julia. https://youtu.be/rZS2LGiurKY https://youtu.be/rZS2LGiurKY Alan (co-author of Julia) mentions that only Swift and Julia make the cut for ML, according to Google. I do like Swift and I’m willing to learn Julia, but most of the ML I see uses Python. Is there any traction for Julia or Swift, or is it mostly aspirational at the moment?

saboot 7y ago
Can anyone comment on how well developed the CUDA support is on Julia? Compared to Python? I'm using Python/Numba for my scripts.
- idiliv 7y ago
  From my experience (and this seems to be a general trend with Julia projects) CUDA support in Julia - is not completely stable - is very fast moving - has a low learning curve - is highly amenable to your own tweaks and hacks
xamdam 7y ago
Curious what your source for "make the cut" is. I have my own interpretation, but would like to see the original.
- nexus7 7y ago
  As written above, the cut is this evaluation -- https://github.com/tensorflow/swift/commit/028f245ef3ca735b18379e7340e08a52a787bd5b#diff-bb05f2ec01e7a0e33b23298eabf44dc5 https://github.com/tensorflow/swift/commit/028f245ef3ca735b1... ... written by Chris Lattner when he joined Google Brain and found Python lacking as a language for doing ML work. He evaluated a bunch of alternatives and decided that Swift would be a good choice, so he's now working with a team to explore that. As far as Google at large is concerned, there was no cut of anything. Google keeps producing new ML code in Python and new ML frameworks in Python on a daily basis
anonymousJim12 7y ago

konz 7y ago

Previous discussion: https://news.ycombinator.com/item?id=19933274 https://news.ycombinator.com/item?id=19933274

xamdam 7y ago
Yeah, just noticed. Posted this (even earlier) on Reddit ML it didn't get as much interest

SantalBlush 7y ago

Love Gilbert Strang's original linear algebra lectures on YouTube [1]. They were a huge help while I took the course. In my opinion, he takes great care to motivate each topic and express his train of thought when working through problems. [1] https://m.youtube.com/watch?v=ZK3O402wf1c https://m.youtube.com/watch?v=ZK3O402wf1c

dsiegel2275 7y ago

Strang is an excellent lecturer - his videos for 18.06 (Linear Algebra) were instrumental for me in relearning the subject matter more than twenty years after I last studied it. I used 18.06 to learn the linear algebra necessary to prepare for further study in machine learning. It was sufficient for that purpose, (I got a B and an A in CMU's 10-601 and 10-605 ML courses, respectively) but this 18.065 course is more specifically geared for that purpose. The companion book is quite good, as well.

liopleurodon 7y ago
I second this. His videos are phenomenal