I'm not even sure where to begin with understanding this stuff, although it's just fascinating.
An important thing to realize is that much of deep learning is decades old neural networks research that has for one reason or another become more viable recently.
Your learning will culminate in this course: https://www.udacity.com/course/deep-learning--ud730
Hello World - Machine Learning Recipes #1
Visualizing a Decision Tree - Machine Learning Recipes #2
(Disclaimer: I work on TensorBoard)
One thing that is consistently frustrating about these kind of projects, for machine vision specifically however is that they don't even give you a framework for building your own training sets - which in my view is the most valuable thing you can have. I mean sure, if I want to find cats/flowers etc... in my program, ok fine, but for the vast majority of possible machine vision sets there are specific things you need to find that aren't in the standard trained datasets.
Sure wish "trained network as a service" was a thing.
I'm curious whether this would perform differently than e.g. Keras, on deeper networks.
Relevant file in project: https://github.com/tflearn/tflearn/blob/0.1.0/tflearn/optimi...