RNNs are the most challenging thing to understand in ML

I’ve been thinking about this for a while, and I’m curious if others feel the same. I’ve been reasonably comfortable building intuition around most ML concepts I’ve touched so far. CNNs made sense once I understood basic image processing ideas. Autoencoders clicked as compression + reconstruction. Even time series models felt intuitive once I framed them as structured sequences with locality and dependency over time. But RNNs? They’ve been uniquely hard in a way nothing else has been. It’s not that the math is incomprehensible, or that I don’t understand sequences. I *do*. I understand sliding windows, autoregressive models, sequence-to-sequence setups, and I’ve even built LSTM-based projects before without fully “getting” what was going on internally. What trips me up is that RNNs don’t give me a stable mental model. The hidden state feels fundamentally opaque i.e. it's not like a feature map or a signal transformation, but a compressed, evolving internal memory whose semantics I can’t easily reason about. Every explanation feels syntactically different, but conceptually slippery in the same way.