If you've no experience with ML stuff, you might want to start with Andrew Ng's course, which has a small bit on neural networks (MLP and backpropagation), with examples in Matlab/Octave. I found it useful, along with "Make Your Own Neural Network" by Tariq Rashid. Very good intro to coding MLPs directly in Python.

Ng's course would be a great place to start, imho - though I am a bit biased: I started out my journey by taking the ML Class in 2011. Lot's of "concretely's" strewn about!

Anyhow, it was a great introduction, and light on the calc (but more emphasis on probability and linear algebra). If you have matlab or octave experience, it will also help (I didn't - the revelation of having a vector primitive was wonderful once I got the swing of it, though).

Note again, though, that I took the ML Class - not the Coursera version; I have heard that they are identical, but it has been 5+ years since I took it, too.

Or how does it compare to udacity's intro to machine learning? https://www.udacity.com/course/intro-to-machine-learning--ud... it was recommended in https://medium.com/learning-new-stuff/machine-learning-in-a-...

If you're getting started I totally recommend Andrew NGs course...

Strongly agree. Andrew's course is a great choice to take before this one, if you have the time. It's not a prerequisite however.

The Udacity course has a very different aim - it covers much less territory and takes much less time. If you're just wanting to get a taste of deep learning, it's a very good option, but it's not a great platform to build longer term capability on IMHO.