Matrix Calculus for Deep Learning • Hacker News Board

Re: Matrix Calculus for Deep Learning

#81

Post by parrt » Wed, Jan 31, 2018, 6:44 PM UTC

calebh wrote: post #57

parrt wrote: post #34

Earlier quoted context omitted.

Wow! Great little calculator. Thanks for pointing us at it.

You should add a link in the resources section

Done. Added a link.

Re: Matrix Calculus for Deep Learning

#82

Post by 2paisay » Wed, Jan 31, 2018, 7:23 PM UTC

jph00 wrote: post #4

Jeremy 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…

ERRATA: In the start of Matrix Calculus section, shouldn't the first value in gradient of "g" be 2 instead of 1 i.e., [2,8y^7]

Re: Matrix Calculus for Deep Learning

#83

Post by marrowgari » Thu, Feb 01, 2018, 10:05 PM UTC

ink404 wrote: post #10

marrowgari wrote: post #3

If 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.

https://www.coursera.org/learn/machine-learning this one?

this one... https://www.coursera.org/learn/neural-networks-deep-learning...

Re: Matrix Calculus for Deep Learning

#84

Post by marrowgari » Thu, Feb 01, 2018, 10:08 PM UTC

ransom1538 wrote: post #16

marrowgari wrote: post #3

If 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.

It is also all free on youtube: https://www.youtube.com/watch?v=UzxYlbK2c7E

This is great. But I find the advantage of Coursera is that it incorporates quizzes and programming homework into the lectures reinforcing the learning. Also, the material is updated and more relevant to today's ML problems then his 2008 lectures on youtube.