I have none of these "The Little" books in my bookshelf, and I would gladly purchase a bunch of them. Are there any such deals anywhere?
The Little Learner: A Straight Line to Deep Learning
61–70 of 102 posts
Re: The Little Learner: A Straight Line to Deep Learning
#62Wow, I would really like to get this book to complete my set of the Little Schemer, the Seasoned Schemer and the Reasoned Schemer. But 55$ is almost twice as expensive as the other books. Yes, it is bigger than those but still... Have to think about this for a while :)
There's also "The Little Typer" and "The Little Prover."
yeah, I just realized that too. screw it, I'll just grab all of them ;-)
Re: The Little Learner: A Straight Line to Deep Learning
#63Earlier quoted context omitted.
Love the beauty of Scheme, but lack of types (mypy Python / Java dev/ TS dev) infuses fear to me. I think with a little of patience I can get by.
You should try typed Racket!
Will do. I have also found that 3 days ago
https://github.com/zdimension/hm-infer-scheme
fantastic for research
Re: The Little Learner: A Straight Line to Deep Learning
#64Earlier quoted context omitted.
In the same series, there's also "A Little Java" by Matthias Felleisen, one of the creators of Racket (a popular Lisp). https://mitpress.mit.edu/9780262561150/a-little-java-a-few-p...
And The Little MLer .
Which, it seems, is out of print and I guess because a lot of people want a complete set of this series, costs > 500USD to buy used?? Woah. I should get my hands on the others while they're still in print.
Re: The Little Learner: A Straight Line to Deep Learning
#65Earlier quoted context omitted.
This is what the book has to say (part of a foreword by Peter Norvig): > Maybe, maybe not. But even if you use a machine learning toolkit like TensorFlow or PyTorch, what you will take away from this book is an appreciation for how the fundamentals work.
I think all serious researchers have implemented core Deep Learning algorithms from scratch. I know I have. There are two books that do exactly this: 1. Deep Learning from Scratch 2. Data Science from Scratch In these books, you implement each part of the ML/DL pipeline, from scratch, in Python. There is also a GitHub project call Minitorch that teaches you the inner workings of a framework like PyTorch. And then the…
What does "from scratch" really mean? You don't reimplement Python itself, or invent a new GPU hardware, a new CUDA including compiler, etc. You don't reimplement the OS. Where do you draw the line?
Do you reimplement matmul or other basics?
Do you reimplement auto-diff?
Maybe PyTorch or TensorFlow using auto-diff is a good "from scratch" basepoint, without using predefined optimizers, or modules/layers, or anything. Just using the low-level math functions, and then auto-diff.
Re: The Little Learner: A Straight Line to Deep Learning
#66Someone who has access to O'Reilly and other free source to learn ML/AI e.g. Fast.ai, is there any advantage of buying this?
'Little..' series books are famous for their pedagogical style: small bit of concepts, presented as a dialogue, making you think at each step, in a well thought out order. It will appeal to anyone who wants to build it out themselves, understand the 'big' ideas in the field. Skim through the second chapter to see how this will be done: they define line function in inverted way where slope and intercept are taken as parameters later. Feels weird but exactly the mental model shift we should be doing when thinking about ML.
Read the preface. A very high praise coming from Guy Steele Jr and Peter Norvig.
At the same time, a warning! Scheme based introductions don't appeal to everyone. Some people feel way out of comfort zone in coding with it (which is sad because it is much simpler). Also, the utilitarian appeal is low: it won't right away see a step change in your Pytorch knowledge or whatever. The appeal of these books is to think deeply about fundamental ideas by implementing them in simplest language without too much help.
In short, YMMV. But if you have a long term view it might help you a lot than sort of currently fashionable trends. (Though I must admit that fast.ai is not just a flavor of the season resources but much better!)
Re: The Little Learner: A Straight Line to Deep Learning
#67I went through some of the eBook providers but none seem to be offering a PDF version. Did anyone notice if a PDF version was for sale?
I bought "The Reasoned Schemer" as physical book and searched for a way to buy it as a PDF version, but could find it nowhere (also had no problem to pay twice for the book).
The Kindle version of it does not even have fixed table cell width for the layout of the conversation.
German book website buecher.de has "The Little Learner" to preorder as ePUB with Adobe DRM. I don't know, if you can buy digital books on this site outside of Germany. Also notice, that copy-protected digital books on this website are highly unusual; most of the books are with watermark instead, if at all.
I have no clue why a book with pedagogical background and custom layout, has copy-protection and no print-quality PDF version (but instead one with poor layout).
Re: The Little Learner: A Straight Line to Deep Learning
#68Earlier quoted context omitted.
You should try typed Racket!
Will do. I have also found that 3 days ago https://github.com/zdimension/hm-infer-scheme fantastic for research
CL is decent in this regard: http://alhassy.com/TypedLisp
Re: The Little Learner: A Straight Line to Deep Learning
#69Earlier quoted context omitted.
I think all serious researchers have implemented core Deep Learning algorithms from scratch. I know I have. There are two books that do exactly this: 1. Deep Learning from Scratch 2. Data Science from Scratch In these books, you implement each part of the ML/DL pipeline, from scratch, in Python. There is also a GitHub project call Minitorch that teaches you the inner workings of a framework like PyTorch. And then the…
What does "from scratch" really mean? You don't reimplement Python itself, or invent a new GPU hardware, a new CUDA including compiler, etc. You don't reimplement the OS. Where do you draw the line? Do you reimplement matmul or other basics? Do you reimplement auto-diff? Maybe PyTorch or TensorFlow using auto-diff is a good "from scratch" basepoint, without using predefined optimizers, or modules/layers, or anything.…
I don't understand why you are arguing with me.
Yes, in those books, you do implement matmul, auto-diff, etc.
Re: The Little Learner: A Straight Line to Deep Learning
#70I helped proofread this book, definitely check it out.
You are the best!