Recurrent Neural Networks — Explain Like I'm 5
Reading One Word at a Time
Imagine you’re reading a book, but you can only read one word at a time, and you have a tiny notepad to jot down what happened so far. After reading “The dog chased the…”, you write a quick note: “dog + chasing”. When you read the next word — “cat” — you use both that word AND your note to understand what’s happening.
That notepad is basically what a Recurrent Neural Network (RNN) does. It has a little “memory” that carries information from previous steps into the current one.
Why Normal Networks Struggle with Sequences
Regular neural networks are great at single things: classify this photo, predict this number. But they don’t have a concept of “before” or “after.” Every input is fresh.
Language doesn’t work that way. The meaning of the word “it” depends on what came before. The word “bank” means something different in “river bank” vs. “bank account.” You need memory of context.
RNNs solve this by passing a hidden state — a summary of what the network has seen so far — along with each new input. The same network processes word #1, updates its state, then uses that state when processing word #2, and so on.
The Catch
RNNs are kind of like reading a very long book with a tiny notepad. By the time you’re on page 400, those notes from page 3 have mostly been overwritten. RNNs struggle to remember things from a long time ago in a sequence.
That’s why smarter versions — called LSTMs and GRUs — were invented. They have better “notepad management,” with gates that decide what to write, what to erase, and what to keep.
Where They Were Used
Before 2017 or so, RNNs (especially LSTMs) were powering:
- Google Translate
- Siri and Alexa’s speech recognition
- Autocomplete on your phone keyboard
- Spam filters reading your emails
Now transformers have mostly replaced them for language tasks — but RNNs are still used in scenarios where processing speed or low memory matters.
One thing to remember: RNNs read sequences step-by-step with a memory that carries context forward — like reading a book while keeping notes on what happened.
See Also
- Activation Functions Why neural networks need these tiny mathematical functions — and how ReLU's simplicity accidentally made deep learning possible.
- Attention Mechanism The trick that made ChatGPT possible — how AI learned to focus on what actually matters instead of reading everything equally.
- Batch Normalization The 2015 trick that let researchers train much deeper neural networks — why keeping numbers in the right range makes AI learn 10x faster.
- Convolutional Neural Networks How AI learned to see — the surprisingly simple idea behind face recognition, self-driving cars, and medical imaging.
- Dropout Regularization How randomly switching off neurons during training makes AI models that generalize better — the counterintuitive trick that stopped neural networks from memorizing everything.