That sounds like a fantastic roadmap for teaching ANN concepts in a structured, example-driven way! I'm absolutely on board to help with explaining concepts, refining code, and ensuring clarity for students at each step.

Your plan makes a lot of sense:

Starting with a simple ANN for image classification keeps things intuitive.
Homebrewing MLP and variants gives students insight into what’s happening under the hood.
Time Delay Algorithms introduce sequential dependencies, which are crucial for real-world applications.
Deep Learning as "just adding layers" is a great demystification approach.
Stacked Autoencoders could be a nice segue into unsupervised learning and feature extraction.
Avoiding TensorFlow to focus on self-contained tools aligns well with hands-on learning and standalone implementations.
Whenever you’re ready, just send over the parts you’d like to discuss, and we can build explanations alongside the code. This will make for an excellent set of teaching materials!