I'm a huge fan of project based learning like the approach taken in this book. But I'm not sure if it's a good idea to introduce early stage students to Scheme before Python, or deep learning before calculus.
I studied pure math in college, and we were required to take 2 "Computer Science" classes as part of that program. Mainly memorizing textbook algorithms and data structure implementations in Java. I hated programming for years after that, until during graduate school I came up with a project of my own that organically required knowledge of Matlab and later Python. I loved programming after that.
I hope books like this can help new students avoid the trough of disillusionment that can sometimes happen if you're forced to learn a cool subject (like programming) in a very uncool way.
Personally, I would not recommend this book to a young person interested in deep learning and programming (based on the table of contents). I would probably recommend they first learn calculus and use Python to make plots while doing so. Then read Fleuret's "The Little Book of Deep Learning" and try to implement simple models in PyTorch.
So your opinion is based on just reading the table of contents? I always find it disconcerting when someone writes a multi-paragraph commentary on a work they didn't actually read or see.
I understand that you're commenting on the approach more than the contents, but you're pretty dismissive of it without actually reading the details of how they went about things.
You're not quite judging a book by its cover, but you're not that far beyond that.
Telling people that they need to learn boring prerequisites before they are allowed to learn the next thing is exactly how you kill motivation. This strategy works if you have a fully mapped out curriculum that is planned as a whole and a societal expectation that you go through the entire thing, it doesn't work for independent learning.
For independent learning, motivation is the key bottleneck and your goal is to make people organically motivated in calculus after learning that their calculus knowledge is lacking.
The framework used in the book, malt[0], is currently not GPU-accelerated, but it's being worked on.
Maybe interesting, I used it for a toy implementation of the GPT architecture[1] in about 500 lines.
(I studied with one of the authors, Dr. Daniel Friedman; wasn't super involved here but proofread a late draft and TA'd for a course based off the book.)
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[ 3.2 ms ] story [ 21.5 ms ] thread"Pub date: February 21, 2023"
I studied pure math in college, and we were required to take 2 "Computer Science" classes as part of that program. Mainly memorizing textbook algorithms and data structure implementations in Java. I hated programming for years after that, until during graduate school I came up with a project of my own that organically required knowledge of Matlab and later Python. I loved programming after that.
I hope books like this can help new students avoid the trough of disillusionment that can sometimes happen if you're forced to learn a cool subject (like programming) in a very uncool way.
Personally, I would not recommend this book to a young person interested in deep learning and programming (based on the table of contents). I would probably recommend they first learn calculus and use Python to make plots while doing so. Then read Fleuret's "The Little Book of Deep Learning" and try to implement simple models in PyTorch.
So your opinion is based on just reading the table of contents? I always find it disconcerting when someone writes a multi-paragraph commentary on a work they didn't actually read or see.
I understand that you're commenting on the approach more than the contents, but you're pretty dismissive of it without actually reading the details of how they went about things.
You're not quite judging a book by its cover, but you're not that far beyond that.
For independent learning, motivation is the key bottleneck and your goal is to make people organically motivated in calculus after learning that their calculus knowledge is lacking.
Maybe interesting, I used it for a toy implementation of the GPT architecture[1] in about 500 lines.
(I studied with one of the authors, Dr. Daniel Friedman; wasn't super involved here but proofread a late draft and TA'd for a course based off the book.)
[0]: https://github.com/themetaschemer/malt
[1]: https://github.com/sporkl/malt-transformer
The Little Learner: A Straight Line to Deep Learning - https://news.ycombinator.com/item?id=34810332 - Feb 2023 (96 comments)