Re:Tensorflow and Deep Learning, Without a PhD, Martin Gorne...
by agibsonccc on Sunday November 06, 2016 @02:50AM
(#12884060)
Thanks for the feedback! I'll share this with my editor and see what we can do.
Re:Tensorflow and Deep Learning, Without a PhD, Martin Gorne...
by globuous on Sunday November 06, 2016 @09:28AM
(#12885208)
I don't know what my thoughts on the topic are worth since I haven't opened your book. But from parent, and as someone that does have a decent math background, what might be easiest/most effective could be a simple math appendix. Many book have those, to name a few:
-https://www.amazon.com/Paul-Wilmott-Quantitative-Finance-Set...
-https://www.amazon.com/Short-Course-General-Relativity/dp/03...
Re:Tensorflow and Deep Learning, Without a PhD, Martin Gorne...
by imakecomments on Sunday November 06, 2016 @08:58AM
(#12885077)
Glad I could provide some feedback. I think what you're aiming to do is exceptional. Keep up the good work.
In my opinion it wouldn't be too difficult or much effort to define what these mathematical objects are and show basic examples with basic computations to solidify the concepts. The notion of gradient descendent & derivates (or partial derivatives) isn't that difficult to understand and could be easily explained in a page or less.
For example when you discuss the Outer Product:
"This is known as the “tensor product” of two input vectors. We take each element of a column vector and multiply it by all of the elements in a row vector creating a new row in the resultant matrix."
It would be nice for the beginner to see an example of this and as stated it wouldn't take much space in the book to provide one. I think these sort of things would differentiate your book from others. If you made it more friendly more 'practitioners' would be willing to read/use it end-to-end.