Well, hard to say if it is a better resource. But it will appeal to those who are in a hurry (and yes, it is okay to be in a hurry). For example, fast.ai first chapter will have you build a dogs vs cats classifier. But how do they build it? By doing `from fastai.vision import *`. They justify it by claiming to use Teaching the whole game approach. If you are a somewhat experienced engineer who already knows Python, what is Jupyter then this approach will get you started quickly. For someone who feels did I spend my time/money but nothing exciting is happening yet, this is a good start. But for some it is crazy and makes them feel even more scared (what is happening with all this imports, how does it work).
My criticism with fast.ai, (I am part time educator), is that this approach is an information overload and poor sequencing. Their comparison with Teach whole game approach is flawed because a game of, say Football, is essentially simple. So you can say just start kicking around. But we don't teach chess this way. It is accepted that you have to spend some time upfront to learn the rules before you can play even simple game. Sure one need not learn castling or en-passant upfront. But you get the drift.
This book (looking at the preview chapters) is going to follow the lego blocks approach or bottoms up approach to build it. For me, this is correct way to teach supervised ML focussed on neural networks and deep learning. We have a problem of too many library plumbers in the ML field currently. People who can piece together library function calls without knowing why it is working. But this house of cards is not sustainable strategy to build AI based application over long term.
Long story short, the book will need patience but that patience will be worth it!
I'm a Lisper (used Common Lisp, Racket/Scheme, and Clojure) and a math graduate student, and am interested in learning more about ML and DL so that I could potentially use them in research in the future, to come up with constructions and counter-examples, so I may have some time in my hand. I have always been a fan of Dan Friedman, so I'm definitely thinking about getting this book.
I found out that fast.ai seems to be of this approach that you mentioned. Since you are an educator, if I'm interested in learning more about ML and DL (as well as the math behind and implementation), which book(s) would you recommend me to study (I have some experience with Andrew Ng's coursera course many years ago)? I did some research here and people seem to recommend different books...
My criticism with fast.ai, (I am part time educator), is that this approach is an information overload and poor sequencing. Their comparison with Teach whole game approach is flawed because a game of, say Football, is essentially simple. So you can say just start kicking around. But we don't teach chess this way. It is accepted that you have to spend some time upfront to learn the rules before you can play even simple game. Sure one need not learn castling or en-passant upfront. But you get the drift.
This book (looking at the preview chapters) is going to follow the lego blocks approach or bottoms up approach to build it. For me, this is correct way to teach supervised ML focussed on neural networks and deep learning. We have a problem of too many library plumbers in the ML field currently. People who can piece together library function calls without knowing why it is working. But this house of cards is not sustainable strategy to build AI based application over long term.
Long story short, the book will need patience but that patience will be worth it!