Is it working out well for tech pdf's and books? Once I saw the form factor I realized it would be better than an iPad (less heavy, better battery) and that is the use case I am looking at. Might wait till v2.
From what I understand the iPad mini weighs the same with a slightly larger screen. Has their matte display option.
I have big pockets so for me it’s probably pocketable. But I still would have a phone. So I’ve got a duo on order with a year lease so I’m not stuck if I don’t think it’s great.
I have to confess to considering a mini with cellular instead of a phone altogether though.
It's a nice treatment of the subject, but I wish he would post a conventionally-formatted .PDF that puts every page on a full sheet of standard printer paper.
I'd note that the Fleuret books gives the foundation for basic deep learning whereas the OP seems to give the necessary foundations for Transformers and related machinery. These later addition what is now "AI" are in total at least as complicated as the original deep learning concepts. Not that just learning basic neural networks but the two book don't appear to cover the same ground.
Only had a quick skim, but looks like the content of this book has very little to do with transformers. Might be more relevant for diffusion models (image/video generation) though.
My full stack JS jockey buddy studied this in the nascent days of LLMs to prep for an interview and is now generationally wealthy. Sliding doors moment for sure...
The very first formula in the text, explicitly described as "as simple as possible," names its variable ê1 with no explanation given. I think this paper needs another pass.
That's absolutely standard notation for a unit basis vector. I haven't read the prerequisites carefully, so if it addressed to the general public maybe?
It's just giving a vector a name. It also names ê₂. These particular names are very standard and familiar to most people who've met vectors before.
You can look at those vectors and see that the position of the 1 is the subscript, without explanation. The ^ could do with an explanation that it means unit vector, but if you've met vectors before you likely know that (maybe that's elsewhere in the book, but a quick search didn't find it).
What do you mean "no explanation given"? It says: "In 2 dimensions, the standard (or identity) basis vectors are ê1 = [ 1 0 ] and ê2 = [0 1]." Seems pretty clear to me he's defining ê1 and ê2, or rather telling us that he's using this very common way of naming the basis vectors.
Is there any one who have recommended books on the attention architecture specifically? I have been interested in that for a long time but never gotten around to actually studying it.
I'm v curious how you're incorporating this into your workflow. Is it that when you want to implement something you can call out to these books as rigorous reference material for your agents? I'd never thought of doing such a thing and am curious how you're doing it and how it's worked.
From the Dust jacket:
The apocalypse will be televised!
You know what’s worse than breaking up with your girlfriend? Being stuck with her prize-winning show cat. And you know what’s worse than that? An alien invasion, the destruction of all man-made structures on Earth, and the systematic exploitation of all the survivors for a sadistic intergalactic game show. That’s what.
READ MORE
Join Coast Guard vet Carl and his ex-girlfriend’s cat, Princess Donut, as they try to survive the end of the world—or just get to the next level—in a video game–like, trap-filled fantasy dungeon. A dungeon that’s actually the set of a reality television show with countless viewers across the galaxy. Exploding goblins. Magical potions. Deadly, drug-dealing llamas. This ain’t your ordinary game show.
Welcome, Crawler. Welcome to the Dungeon. Survival is optional. Keeping the viewers entertained is not.
My favorite little book is François Fleuret's: The Little Book of Deep Learning https://fleuret.org/francois/lbdl.html
I love how that site offers a phone-formatted PDF as an option. How thoughtful!
i got duo to increase my reading.
Is it working out well for tech pdf's and books? Once I saw the form factor I realized it would be better than an iPad (less heavy, better battery) and that is the use case I am looking at. Might wait till v2.
From what I understand the iPad mini weighs the same with a slightly larger screen. Has their matte display option.
I have big pockets so for me it’s probably pocketable. But I still would have a phone. So I’ve got a duo on order with a year lease so I’m not stuck if I don’t think it’s great.
I have to confess to considering a mini with cellular instead of a phone altogether though.
My old crappy eyes need big screens for this. I have one on order. I’m ambivilating over the screen crack issue.
You may enjoy the reflow feature on Koreader. It fits a pdfs content into a screenwidth single column layout. That text is also zoomable.
It's a nice treatment of the subject, but I wish he would post a conventionally-formatted .PDF that puts every page on a full sheet of standard printer paper.
I immediately wanted the same thing, so I made it. The book is licensed Creative Commons BY-NC-SA 4.0 International License, so here you go.
https://cdn.filepost.dev/file/filepost/uploads/15/1578f21219...
Thanks!
I'd note that the Fleuret books gives the foundation for basic deep learning whereas the OP seems to give the necessary foundations for Transformers and related machinery. These later addition what is now "AI" are in total at least as complicated as the original deep learning concepts. Not that just learning basic neural networks but the two book don't appear to cover the same ground.
Only had a quick skim, but looks like the content of this book has very little to do with transformers. Might be more relevant for diffusion models (image/video generation) though.
My full stack JS jockey buddy studied this in the nascent days of LLMs to prep for an interview and is now generationally wealthy. Sliding doors moment for sure...
The very first formula in the text, explicitly described as "as simple as possible," names its variable ê1 with no explanation given. I think this paper needs another pass.
That's absolutely standard notation for a unit basis vector. I haven't read the prerequisites carefully, so if it addressed to the general public maybe?
It is perhaps addressed to a different crowd?
It's just giving a vector a name. It also names ê₂. These particular names are very standard and familiar to most people who've met vectors before.
You can look at those vectors and see that the position of the 1 is the subscript, without explanation. The ^ could do with an explanation that it means unit vector, but if you've met vectors before you likely know that (maybe that's elsewhere in the book, but a quick search didn't find it).
What do you mean "no explanation given"? It says: "In 2 dimensions, the standard (or identity) basis vectors are ê1 = [ 1 0 ] and ê2 = [0 1]." Seems pretty clear to me he's defining ê1 and ê2, or rather telling us that he's using this very common way of naming the basis vectors.
Wow, this book is surprisingly readable! I like how he starts from basics of linear algebra.
Are there any deep learning books with some problems to solve ?
+1. I only learn from doing exercises.
Someone should add it to the hacker news curated paper list! Along side the video and book list.
Interesting!
Is there any one who have recommended books on the attention architecture specifically? I have been interested in that for a long time but never gotten around to actually studying it.
Not a book, but 3blue1brown's video on attention is pretty great: https://www.youtube.com/watch?v=eMlx5fFNoYc
formal algorithms for transformers provides perhaps the most succinct introduction to attention I can imagine https://arxiv.org/pdf/2207.09238
Appreciated, I drop all of these into a RAG folder for when I need them later on in projects.
8Gb of whitepapers/books...
Can you share what you have so far in that folder?
They spell it out quite clearly: "8Gb of whitepapers/books..."
I'm v curious how you're incorporating this into your workflow. Is it that when you want to implement something you can call out to these books as rigorous reference material for your agents? I'd never thought of doing such a thing and am curious how you're doing it and how it's worked.
Holy..
Care to share the list of titles in a gist?
We are recommending books for HackerBooks.org I see. Good, this is why I have come here also.
Let me tell you about one of the greatest book series of our generation: Dungeon Crawler Carl, By Matt Dinniman
https://mattdinniman.com/books/
From the Dust jacket: The apocalypse will be televised!
You know what’s worse than breaking up with your girlfriend? Being stuck with her prize-winning show cat. And you know what’s worse than that? An alien invasion, the destruction of all man-made structures on Earth, and the systematic exploitation of all the survivors for a sadistic intergalactic game show. That’s what. READ MORE
Join Coast Guard vet Carl and his ex-girlfriend’s cat, Princess Donut, as they try to survive the end of the world—or just get to the next level—in a video game–like, trap-filled fantasy dungeon. A dungeon that’s actually the set of a reality television show with countless viewers across the galaxy. Exploding goblins. Magical potions. Deadly, drug-dealing llamas. This ain’t your ordinary game show.
Welcome, Crawler. Welcome to the Dungeon. Survival is optional. Keeping the viewers entertained is not.
Published sept 21, 2020 RePublished Aug 27, 2024
The street marketing around this book is frankly baffling, do you suppose this is relevant?