Matrix Calculus for Deep Learning
parrt.cs.usfca.edu
Matrix Calculus for Deep Learning
1–10 of 84 posts
Re: Matrix Calculus for Deep Learning
#2Bookmarked, this is a very nice reference I will need to read in greater depth.
Re: Matrix Calculus for Deep Learning
#3If you're looking at this with the intention of getting started in Deep Learning and feeling overwhelmed by the math then Andrew Ng offers a great course on Coursera that goes over all of the formulas needed to calculate the forward propagation, loss computation, backward propagation, and gradient descent. Highly recommend it for anyone interested in breaking into the field of machine learning.
Re: Matrix Calculus for Deep Learning
#4Jeremy here. Here to answer any questions or comments that you have.
But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device to such an extent that he ended up creating a new tool for generating fast, mobile-friendly math-heavy texts: https://github.com/parrt/bookish . (We tried Katex, Mathjax, and pretty much everything else but nothing rendered everything properly).
I've never found anything that introduces the necessary matrix calculus for deep learning clearly, correctly, and accessibly - so I'm happy that this now exists.
Re: Matrix Calculus for Deep Learning
#5Jeremy here. Here to answer any questions or comments that you have. But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device to such an extent that he ended up creating a new tool for generating fast, mobile-friendly math-heavy texts: https://github.com/parrt/bookish . (We tried Katex, Mathjax, and prett…
What do you think about the index notation physicists use for tensor calculus?
Re: Matrix Calculus for Deep Learning
#6Jeremy here. Here to answer any questions or comments that you have. But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device to such an extent that he ended up creating a new tool for generating fast, mobile-friendly math-heavy texts: https://github.com/parrt/bookish . (We tried Katex, Mathjax, and prett…
This is really great, thanks.
Re: Matrix Calculus for Deep Learning
#7Jeremy here. Here to answer any questions or comments that you have. But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device to such an extent that he ended up creating a new tool for generating fast, mobile-friendly math-heavy texts: https://github.com/parrt/bookish . (We tried Katex, Mathjax, and prett…
Terence here. Jeremy's role was critical in terms of direction and content for the article. Who better than he to describe the math needs for deep learning. :)
Re: Matrix Calculus for Deep Learning
#8Jeremy here. Here to answer any questions or comments that you have. But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device to such an extent that he ended up creating a new tool for generating fast, mobile-friendly math-heavy texts: https://github.com/parrt/bookish . (We tried Katex, Mathjax, and prett…
Typographic advice: the body text has very long lines in a desktop browser, which makes it a bit slow and tiring to read. I’d say the ideal is somewhere between 1/2 and 2/3 this length. I’d recommend keeping the same width on screen but bumping the font size up by 30%.
As an extra minor nit, italicizing functions like sin, etc. is also somewhat unconventional in mathematical typesetting.
Re: Matrix Calculus for Deep Learning
#9Jeremy here. Here to answer any questions or comments that you have. But more importantly - I need to mention that Terence Parr did nearly all the work on this. He shared my passion for making something that anyone could read on any device to such an extent that he ended up creating a new tool for generating fast, mobile-friendly math-heavy texts: https://github.com/parrt/bookish . (We tried Katex, Mathjax, and prett…
Typographic advice: the body text has very long lines in a desktop browser, which makes it a bit slow and tiring to read. I’d say the ideal is somewhere between 1/2 and 2/3 this length. I’d recommend keeping the same width on screen but bumping the font size up by 30%. As an extra minor nit, italicizing functions like sin , etc. is also somewhat unconventional in mathematical typesetting.
Thanks for the advice!
Re: Matrix Calculus for Deep Learning
#10If you're looking at this with the intention of getting started in Deep Learning and feeling overwhelmed by the math then Andrew Ng offers a great course on Coursera that goes over all of the formulas needed to calculate the forward propagation, loss computation, backward propagation, and gradient descent. Highly recommend it for anyone interested in breaking into the field of machine learning.