Think Linear Algebra (2023) (opens in new tab)

(allendowney.github.io)

220 pointstamnd15d ago33 comments

33 comments

Allen Downey (author of the above) has a number of books on computer science-y things. You can buy hardcopies but I think all of them are also just freely available.

Here's a few:

Think Complexity

https://github.com/AllenDowney/ThinkComplexity2

Think DSP

https://github.com/AllenDowney/ThinkDSP

Think Stats

https://github.com/AllenDowney/ThinkStats/

Think Bayes

https://github.com/AllenDowney/ThinkBayes2/

neves15d ago

BTW, if Allen Downey is reads this, I just want to send some love. I saw the beginning of the Internet. Everybody was full of dreams of the free flow of information and shareable knowledge, which greatest representation was executable code. Now, when we are surrounded by walled gardens and evil billionaires, Allen is always sharing his knowledge for all the world. Thank you.

nosioptar15d ago

Seconded, not only is Downey quite generous, his books are every bit as good, if not better, than expensive counterparts. Think Stats bailed my ass out of failing a stats class because it was so much clearer than the assigned book.

guiambros14d ago

Also:

- Think Python

- Think Data Structures

- Think Java

- Think Perl6 (!)

- Modeling and Simulation in Python

- Probably Overthinking It

And more [1]. He's a prolific writer, and very generous for offering many of them for free. I read several of them online or through O'Reilly, and bought printed copies just to appreciate his work. Really enjoyed Think DSP, Think Complexity, Think Bayes, etc.

[1] https://www.amazon.com/stores/Allen-Downey/author/B001O8NBPS

shibaprasadb14d ago

I love his blog too! Probably overthinking it.

fn-mote15d ago

You missed How to Think Like a Computer Scientist.

Many places on the web. Runestone is probably the most useful like but I’ll leave my favorite classic one below.

http://www.openbookproject.net/thinkcs/python/english3e/

nosioptar15d ago

Here's a newer version (2023 vs 2012).

I'm pretty sure there are also some forks where people adapted the book to other languages than Java or Python.

https://allendowney.github.io/ThinkPython/

s-zeng15d ago

Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside.

That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra

bsoles15d ago

And eigenvectors in the first lesson!

finghin15d ago

I think at the beginning of learning LA I would have benefited from a more broad introduction to the topic by explaining that it is the algebra of transformations, generally linear transformations, and also the art of quantifying those transformations in meaningful ways.

I would have benefited from some more handwaving in this regard (matrix multiplication, eigenvectors and eigenvalues) and less on the mechanics of the operations, before starting on the basic technicalities. But a “lesson” on these topics on day 0 is too soon

emang2315d ago

Beyond regression, I’d like to see chapters on statistical topics like PCA, CCA. This textbook format which interleaves code and prose is the perfect way to show how scikitlearn’s decomposition.cca and decomposition.pca are implemented, e.g. the SVD matrix decomposition, etc.

mangomountain15d ago

I saw a linear algebra “textbook” on Twitter in maybe 2022? It was black background and bright text with a good amount of graphs like someone’s incredibly long blog post. I’ve tried a few times to find it since but haven’t had any luck.

This looks a bit more involved but lovely I think I’ll try it. I read Think Bayes and thought it was great.

rahimnathwani14d ago

'Coding the matrix' has a black cover with white/bright text.

vidro315d ago

what's the deal with the loop example? am i supposed to understand what this represents before going through the material?

kevinwang14d ago

Where's chapter 3?

immanuwell14d ago

Downey's "Think X" series is consistently the on-ramp for people who learned to code before they learned the math, and honestly at this point everything is linear algebra

anthk15d ago

Klong shines there:

https://www.t3x.org/klong/

Quick ref:

https://www.t3x.org/klong/klong-qref.txt.html

Intro:

https://www.t3x.org/klong/klong-intro.txt.html

Klong for K users:

https://www.t3x.org/klong/klong-vs-k.txt.html

fnord7715d ago

I got my hands on a stanford Math 55 textbook and tried to do the exercises in numpy.

The_Blade15d ago

Linear Algebra is dope, as in when we got to apply some mid-level linear to a real business problem and it worked i got high

deadbishop15d ago

What was the problem you solved?

bonsai_spool15d ago

What was the business problem, broadly? How did you apply linear algebra to it?

j / k navigate · click thread line to collapse

33 comments

staplung15d ago

Allen Downey (author of the above) has a number of books on computer science-y things. You can buy hardcopies but I think all of them are also just freely available.

Here's a few:

Think Complexity

https://github.com/AllenDowney/ThinkComplexity2

Think DSP

https://github.com/AllenDowney/ThinkDSP

Think Stats

https://github.com/AllenDowney/ThinkStats/

Think Bayes

https://github.com/AllenDowney/ThinkBayes2/

neves15d ago

nosioptar15d ago

guiambros14d ago

Also:

- Think Python

- Think Data Structures

- Think Java

- Think Perl6 (!)

- Modeling and Simulation in Python

- Probably Overthinking It

[1] https://www.amazon.com/stores/Allen-Downey/author/B001O8NBPS

shibaprasadb14d ago

I love his blog too! Probably overthinking it.

fn-mote15d ago

You missed How to Think Like a Computer Scientist.

Many places on the web. Runestone is probably the most useful like but I’ll leave my favorite classic one below.

http://www.openbookproject.net/thinkcs/python/english3e/

nosioptar15d ago

Here's a newer version (2023 vs 2012).

I'm pretty sure there are also some forks where people adapted the book to other languages than Java or Python.

https://allendowney.github.io/ThinkPython/

s-zeng15d ago

Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside.

That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra

bsoles15d ago

And eigenvectors in the first lesson!

finghin15d ago

emang2315d ago

mangomountain15d ago

This looks a bit more involved but lovely I think I’ll try it. I read Think Bayes and thought it was great.

rahimnathwani14d ago

'Coding the matrix' has a black cover with white/bright text.

vidro315d ago

what's the deal with the loop example? am i supposed to understand what this represents before going through the material?

kevinwang14d ago

Where's chapter 3?

immanuwell14d ago

Downey's "Think X" series is consistently the on-ramp for people who learned to code before they learned the math, and honestly at this point everything is linear algebra

anthk15d ago

Klong shines there:

https://www.t3x.org/klong/

Quick ref:

https://www.t3x.org/klong/klong-qref.txt.html

Intro:

https://www.t3x.org/klong/klong-intro.txt.html

Klong for K users:

https://www.t3x.org/klong/klong-vs-k.txt.html

fnord7715d ago

I got my hands on a stanford Math 55 textbook and tried to do the exercises in numpy.

The_Blade15d ago

Linear Algebra is dope, as in when we got to apply some mid-level linear to a real business problem and it worked i got high

deadbishop15d ago

What was the problem you solved?

bonsai_spool15d ago

What was the business problem, broadly? How did you apply linear algebra to it?

j / k navigate · click thread line to collapse