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I have to echo what others have said: great book idea, but not so hot on the model. I studied linear algebra in college and I could use a refresher for AI, but

I have to echo what others have said: great book idea, but not so hot on the model. I studied linear algebra in college and I could use a refresher for AI, but I don’t want to study math in the traditional way, with convoluted abstract examples that lead nowhere. Hated it back then and hate it now. The prospect of doing it in code makes it infinitely more exciting so you’ve definitely got something going on there there... However, I’m not going to get into another subscription, mainly because I don’t want to check every month to see the state of the project. I’d personally pay you around $40 for an early access fee that also gets me the final product... It’d be a discount from your target price but I’d also be taking a risk (what if you don’t finish it?). I’m favoriting this and will check how it evolves. Worst case, I’ll buy the finished product if the reviews are good!

dragandj 6y ago
Sure! You'll be welcome.
arto 6y ago
You may be looking for this book: https://www.manning.com/books/math-for-programmers https://www.manning.com/books/math-for-programmers
- arcturus17 6y ago
  That looks great, thank you. I’m actually tempted to buy it - have you worked through it?
- KallDrexx 6y ago
  This looks perfect for my case. I think I"m going to order this, thanks!
chongli 6y ago
I don’t want to study math in the traditional way, with convoluted abstract examples that lead nowhere Have you checked out Gilbert Strang's lectures [1]? They're definitely geared toward numerical/applied linear algebra rather than theoretical/abstract. [1] https://www.youtube.com/watch?v=7UJ4CFRGd-U&list=PLE7DDD91010BC51F8 https://www.youtube.com/watch?v=7UJ4CFRGd-U&list=PLE7DDD9101...
- FranzFerdiNaN 6y ago
  I watched it, still either too abstract or too much doing math for the sake of math, not enough explaining why I would want to take the inverse of a determinants eigenvalue or whatever the fuck linear algebra is supposed to be about, because halfway through the course I still had barely a clue what the point was.
  - chongli 6y ago
    Maybe you would get something out of 3blue1brown's series on linear algebra [1]. It will give you an intuitive, geometric interpretation for concepts such as a vector, a basis, an eigenvalue, a linear transformation, etc. As for why you would want to know these things, well, linear algebra is finding applications all over the place these days. Everything from computer graphics to machine learning is jam-packed with linear algebra. And I'm not just talking about basic concepts such as matrix-vector multiplication. The singular value decomposition has applications in discrete optimization, image compression, the PageRank algorithm [2], computer vision [3], and machine learning [2] [4]. Having said that, you may find it very difficult to understand something like SVD without a firm grounding in the topic of vector spaces, linear transformations, spanning, linear (in)dependence, subspaces, eigenvalues, eigenvectors, diagonalization, and determinants. This is why SVD is one of the last things you learn in a linear algebra course (indeed, it's not covered until lecture 29 of Gilbert Strang's course). Ultimately, it all depends on how relevant these things are to you. I would assume (hope) that because you clicked on this HN discussion that you're interested in learning linear algebra because you think it might be useful to you. [1] https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQ... [2] http://www.cs.cornell.edu/courses/cs4850/2010sp/Course%20Notes/Part%202%20SVD-Applications-feb-22-09.pdf http://www.cs.cornell.edu/courses/cs4850/2010sp/Course%20Not... [3] http://cs.rkmvu.ac.in/~sghosh/public_html/nitw_igga/talk.pdf http://cs.rkmvu.ac.in/~sghosh/public_html/nitw_igga/talk.pdf [4] https://medium.com/@jonathan_hui/machine-learning-singular-value-decomposition-svd-principal-component-analysis-pca-1d45e885e491 https://medium.com/@jonathan_hui/machine-learning-singular-v...
  - snapetom 6y ago
    I agree on your assessment of Gilbert Strang's courses. So many here on HN recommend Strang's stuff. However, he's kind of the victim of his own success. If you've taken LA in college in the last 10-15 years, or any online LA class like Khan Academy, they teach from a geometric approach like he advocates and pioneered. Therefore, you're not exactly learning anything new, just a different person saying it; which, of course, might help. But it's still LA for the sake of LA. Mind you, they are very good, and out of all online courses I've seen, the questions are actually hard and make you think and understand on a deeper level. Every other course I've seen have simple, fluff questions, if any. Tim Chartier of Davidson College put out a class on EdX called Applications of Linear Algebra. It's more practical, but I found Part 1 to be too practical and not enough math. It suffers from the opposite problem. Part 2 may be better, but I didn't make it through. Plus, I found his style to be a little woo-woo. You might have some success with it because he actually does talk about real world scenarios. https://www.edx.org/course/applications-of-linear-algebra-part-1 https://www.edx.org/course/applications-of-linear-algebra-pa... I've actually looked at about half a dozen online LA resources that take different approaches. I should do a blog post about them one day.
thomasahle 6y ago
I had the exact opposite experience of linear algebra in college. It seemed like we (CS students) were just wasting our time running matrix algorithms by hand, while the math students were learning an actual theory, allowing them to intuitively understand all the algorithms much more easily.
reese_john 6y ago
Fast.ai has a course on numerical linear algebra which folllows more of a "top-down" approach: https://github.com/fastai/numerical-linear-algebra/ https://github.com/fastai/numerical-linear-algebra/
joexner 6y ago
Couldn't you just subscribe for one month for $9 and see what's ready now?
- arcturus17 6y ago
  I can see the appeal of the lower barrier to entry, but I would need to follow the project every month to see if they’re delivering. Really don’t want to be burdened with that.
  - dragandj 6y ago
    The two books are already at 300 pages (DLFP) and 170 pages (Linear Algebra), if you're wondering how the things are progressing.
ngmc 6y ago
I'm taking a refresher course on linear algebra from UT Austin and love their approach - good mix of algorithms, proofs, exercises, and coding assignments. http://ulaff.net/ http://ulaff.net/

dragandj 6y ago

Sure! You'll be welcome.

arto 6y ago

You may be looking for this book: https://www.manning.com/books/math-for-programmers https://www.manning.com/books/math-for-programmers

arcturus17 6y ago
That looks great, thank you. I’m actually tempted to buy it - have you worked through it?
KallDrexx 6y ago
This looks perfect for my case. I think I"m going to order this, thanks!

chongli 6y ago

I don’t want to study math in the traditional way, with convoluted abstract examples that lead nowhere Have you checked out Gilbert Strang's lectures [1]? They're definitely geared toward numerical/applied linear algebra rather than theoretical/abstract. [1] https://www.youtube.com/watch?v=7UJ4CFRGd-U&list=PLE7DDD91010BC51F8 https://www.youtube.com/watch?v=7UJ4CFRGd-U&list=PLE7DDD9101...

FranzFerdiNaN 6y ago
I watched it, still either too abstract or too much doing math for the sake of math, not enough explaining why I would want to take the inverse of a determinants eigenvalue or whatever the fuck linear algebra is supposed to be about, because halfway through the course I still had barely a clue what the point was.
- chongli 6y ago
  Maybe you would get something out of 3blue1brown's series on linear algebra [1]. It will give you an intuitive, geometric interpretation for concepts such as a vector, a basis, an eigenvalue, a linear transformation, etc. As for why you would want to know these things, well, linear algebra is finding applications all over the place these days. Everything from computer graphics to machine learning is jam-packed with linear algebra. And I'm not just talking about basic concepts such as matrix-vector multiplication. The singular value decomposition has applications in discrete optimization, image compression, the PageRank algorithm [2], computer vision [3], and machine learning [2] [4]. Having said that, you may find it very difficult to understand something like SVD without a firm grounding in the topic of vector spaces, linear transformations, spanning, linear (in)dependence, subspaces, eigenvalues, eigenvectors, diagonalization, and determinants. This is why SVD is one of the last things you learn in a linear algebra course (indeed, it's not covered until lecture 29 of Gilbert Strang's course). Ultimately, it all depends on how relevant these things are to you. I would assume (hope) that because you clicked on this HN discussion that you're interested in learning linear algebra because you think it might be useful to you. [1] https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQ... [2] http://www.cs.cornell.edu/courses/cs4850/2010sp/Course%20Notes/Part%202%20SVD-Applications-feb-22-09.pdf http://www.cs.cornell.edu/courses/cs4850/2010sp/Course%20Not... [3] http://cs.rkmvu.ac.in/~sghosh/public_html/nitw_igga/talk.pdf http://cs.rkmvu.ac.in/~sghosh/public_html/nitw_igga/talk.pdf [4] https://medium.com/@jonathan_hui/machine-learning-singular-value-decomposition-svd-principal-component-analysis-pca-1d45e885e491 https://medium.com/@jonathan_hui/machine-learning-singular-v...
- snapetom 6y ago
  I agree on your assessment of Gilbert Strang's courses. So many here on HN recommend Strang's stuff. However, he's kind of the victim of his own success. If you've taken LA in college in the last 10-15 years, or any online LA class like Khan Academy, they teach from a geometric approach like he advocates and pioneered. Therefore, you're not exactly learning anything new, just a different person saying it; which, of course, might help. But it's still LA for the sake of LA. Mind you, they are very good, and out of all online courses I've seen, the questions are actually hard and make you think and understand on a deeper level. Every other course I've seen have simple, fluff questions, if any. Tim Chartier of Davidson College put out a class on EdX called Applications of Linear Algebra. It's more practical, but I found Part 1 to be too practical and not enough math. It suffers from the opposite problem. Part 2 may be better, but I didn't make it through. Plus, I found his style to be a little woo-woo. You might have some success with it because he actually does talk about real world scenarios. https://www.edx.org/course/applications-of-linear-algebra-part-1 https://www.edx.org/course/applications-of-linear-algebra-pa... I've actually looked at about half a dozen online LA resources that take different approaches. I should do a blog post about them one day.

thomasahle 6y ago

I had the exact opposite experience of linear algebra in college. It seemed like we (CS students) were just wasting our time running matrix algorithms by hand, while the math students were learning an actual theory, allowing them to intuitively understand all the algorithms much more easily.

reese_john 6y ago

Fast.ai has a course on numerical linear algebra which folllows more of a "top-down" approach: https://github.com/fastai/numerical-linear-algebra/ https://github.com/fastai/numerical-linear-algebra/

joexner 6y ago

Couldn't you just subscribe for one month for $9 and see what's ready now?

arcturus17 6y ago
I can see the appeal of the lower barrier to entry, but I would need to follow the project every month to see if they’re delivering. Really don’t want to be burdened with that.
- dragandj 6y ago
  The two books are already at 300 pages (DLFP) and 170 pages (Linear Algebra), if you're wondering how the things are progressing.

ngmc 6y ago

I'm taking a refresher course on linear algebra from UT Austin and love their approach - good mix of algorithms, proofs, exercises, and coding assignments. http://ulaff.net/ http://ulaff.net/