Well I came here because I had the same question, but perhaps my few results could be helpful. Mostly we are talking about some standards here, but I will point out some of the GMM specific content.
The best practical online example I could find in my non-exhaustive search was this Kaggle contest winner and his github. The MATLAB docs have an okay summary, plus MATLAB code if you like that.
The following are math/theory textbooks (for the most part), sprobably none of these will make much sense without some formal education in the subject or incredible motivation, it seems the tutorial writers have no love for GMMs.
I'm guessing you've looked at Elements of Statistical Learning before. There is good material on GMM, but it's sort of scattered throughout the book. Unfortunately, An Introduction to Statistical Learning by the same authors has nothing that I can find about GMMs, or mixture models of any kind.
Pattern Recognition and Machine Learning by Christopher Bishop has a presentation of the math behind GMM and the EM algorithm that was just a little easier for me to read than Elements. Where this text really shines is a much deeper exploration of the EM algorithm, applying it to various other cases.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy has some nice content on other types of mixture models and the general concept of clustering with a mixture model.