3 ms·

The main difference is that "Hands-On ML..." is a legitimately good book.

by st1x7 6y ago

sgt101 6y ago
I think that it's unfair of you just to assert this without giving some specific criticism of this book. I am pretty interested actually as I am currently trying to write a similar book of my own! I can see a lot of difference between this and what I am trying to do, and may differences to "Hands on" but what in particular are you disappointed with here?
- st1x7 6y ago
  The book in the original post and the other book from the same author are short surface-level summaries of the field that don't go in depth at all and don't provide any meaningful new content. It's like someone took their personal notes, formatted them in a pdf and called them a book. It's an embarrasingly transparent cash grab or just something that helps the author's social media presence because they can now say that they "have written an ML book".
  - nchelluri 6y ago
    > It's like someone took their personal notes, formatted them in a pdf and called them a book. Why is that such a bad thing? I like condensed, reader's digests versions of things. Not every text has to break new ground; some of it can be, well, purely educational. Or do you dispute the educational value of the text? Legitimately curious; I haven't read any of the books, but I actually like the way you described the author's text process.
    - sgt101 6y ago
      I agree - "new material" is actually a concern in the context of a text book. I feel that synthesizing and presenting a coherent view of the communities best practice is a very valuable thing. I think (but am very open to correction) that this is now a very hard thing for academics to do - the incentive for writing a text book is very low because they are not esteemed or counted as research? Writing a book like this is very hard.
  - whymauri 6y ago
    >the other book from the same author are short surface-level summaries of the field that don't go in depth at all Isn't this the entire point of the 100 page Machine Learning Book? An introduction for new practitioners?

The main difference is that "Hands-On ML..." is a legitimately good book.

by st1x7 6y ago

The main difference is that "Hands-On ML..." is a legitimately good book.

I think that it's unfair of you just to assert this without giving some specific criticism of this book. I am pretty interested actually as I am currently trying to write a similar book of my own! I can see a lot of difference between this and what I am trying to do, and may differences to "Hands on" but what in particular are you disappointed with here?

st1x7 6y ago
The book in the original post and the other book from the same author are short surface-level summaries of the field that don't go in depth at all and don't provide any meaningful new content. It's like someone took their personal notes, formatted them in a pdf and called them a book. It's an embarrasingly transparent cash grab or just something that helps the author's social media presence because they can now say that they "have written an ML book".
- nchelluri 6y ago
  > It's like someone took their personal notes, formatted them in a pdf and called them a book. Why is that such a bad thing? I like condensed, reader's digests versions of things. Not every text has to break new ground; some of it can be, well, purely educational. Or do you dispute the educational value of the text? Legitimately curious; I haven't read any of the books, but I actually like the way you described the author's text process.
  - sgt101 6y ago
    I agree - "new material" is actually a concern in the context of a text book. I feel that synthesizing and presenting a coherent view of the communities best practice is a very valuable thing. I think (but am very open to correction) that this is now a very hard thing for academics to do - the incentive for writing a text book is very low because they are not esteemed or counted as research? Writing a book like this is very hard.
- whymauri 6y ago
  >the other book from the same author are short surface-level summaries of the field that don't go in depth at all Isn't this the entire point of the 100 page Machine Learning Book? An introduction for new practitioners?