
Understanding Machine Learning: From Theory to Algorithms [pdf] - mindcrime
http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
======
greenpizza13
This looks interesting, but the first page literally says not to post the link
to the PDF, but post to here instead:
[http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/).

~~~
mindcrime
The thing is, I read that, and totally _mis_ read it. I was thinking the
author meant to say "don't post a copy of this PDF to your own server and link
to it". It never occurred to me that he meant "don't link to this PDF". (I
glanced at the URL there and saw that the domain part was the same, and
assumed that the link _was_ the PDF link).

Oh well, if an admin sees this and wants to change the link, that would be
cool. If not, well... what can ya do?

------
cuchoi
Has someone used this book? I am currently doing Abu-Mostafa's Caltech course
(Link for the interested:
[https://work.caltech.edu/telecourse.html](https://work.caltech.edu/telecourse.html))
and I would like to know if it is a good complement.

~~~
mindcrime
I haven't, I just discovered this book and thought it looked useful. The book
I _have_ been getting some mileage out of, even though it's a bit dated, is
_Fundamentals of Neural Networks: Architectures, Algorithms And Applications_
by Laurene Fausett.

[https://www.amazon.com/gp/product/0133341860/ref=oh_aui_deta...](https://www.amazon.com/gp/product/0133341860/ref=oh_aui_detailpage_o08_s00?ie=UTF8&psc=1)

I've also been looking at _Neural Network Design_ by Martin Hagan.

[https://www.amazon.com/gp/product/0971732116/ref=oh_aui_deta...](https://www.amazon.com/gp/product/0971732116/ref=oh_aui_detailpage_o00_s00?ie=UTF8&psc=1)

That one is also freely available online:

[http://hagan.okstate.edu/nnd.html](http://hagan.okstate.edu/nnd.html)

~~~
zump
NN != Statistical machine learning

~~~
mindcrime
Fair enough, but the LfD class that @cuchoi mentioned does include a section
on NN. And there are common principles, like understanding the variance/bias
tradeoff, etc., that are common across many different ML approaches.

