The post discusses the notion that deep learning, often perceived as complex or mysterious, can actually be understood through established concepts in machine learning. In particular, algorithmic stability is highlighted as a compelling explanation for deep learning behaviors, as opposed to purely relying on PAC-Bayes or VC theory. User comments suggest valuable resources like "The StatQuest Illustrated Guide to Machine Learning" by Josh Starmer for clarifying complex ideas, emphasizing the need for simpler, intuitive explanations in teaching machine learning concepts. Additionally, comments touch on how deep learning might relate to simpler models through soft preferences in hypothesis space, and address the intricacies of penalizing complexity in model training—aspects relevant for aspiring data scientists and educators.