How RNNs Work — Remembering Previous States in Sequential Data

A normal neural network treats each input mostly as a fixed snapshot.

But many problems are not snapshots.

Text, speech, and time-series data depend on order.

That is why RNNs exist.

Core Idea

A Recurrent Neural Network is designed for sequential data.

It does not only look at the current input.

It also carries information from previous steps.

That carried information is called the hidden state.

So an RNN can process a sequence one step at a time while keeping memory of what came before.

The Key Structure

A simple RNN flow looks like this:

Previous Hidden State + Current Input → New Hidden State → Output

More compactly:

RNN = current input + previous state

At each time step:

h_t = f(x_t, h_{t-1})

Where:

• x_t = input at the current time step
• h_{t-1} = previous hidden state
• h_t = updated hidden state

This recurrence is the core mechanism.

Implementation View

At a high level, an RNN processes a sequence like this:

initialize hidden state

for each time step in the sequence:
    read current input

    combine it with previous hidden state

    update hidden state

    optionally produce output

return final output or all outputs

This is why RNNs are useful for ordered data.

The model can carry context forward.

It does not restart from zero at every step.

Concrete Example

Imagine a sentence:

I love machine learning

A basic feedforward network may process words as independent inputs.

But an RNN reads them in order.

Step 1:

I

Step 2:

I love

Step 3:

I love machine

Step 4:

I love machine learning

At each step, the hidden state carries previous context.

That is how the model remembers earlier words while reading later ones.

Standard Neural Network vs RNN

This comparison makes the difference clear.

Standard neural network:

• processes fixed-size input
• has no built-in memory across time
• works well for static feature vectors
• does not naturally model order

RNN:

• processes sequences step by step
• carries hidden state forward
• models temporal or ordered dependence
• fits text, speech, and time-series tasks

The key difference is state.

A standard network transforms input.

An RNN transforms input while remembering previous context.

Why Hidden State Matters

The hidden state is the memory of the RNN.

It is not memory in the human sense.

It is a vector that summarizes previous information.

At each step, the hidden state is updated.

That updated state influences the next step.

This lets the model capture patterns like:

• word order
• temporal trends
• repeated signals
• dependency across earlier and later inputs

Without hidden state, the sequence becomes just a list of disconnected inputs.

Why Deep RNNs Exist

A basic RNN can model sequences.

But some patterns are more complex.

A Deep RNN stacks recurrent layers.

That allows the model to build richer sequence representations.

Basic RNN:

• one recurrent layer
• simpler sequence modeling
• easier to understand
• limited representational depth

Deep RNN:

• multiple recurrent layers
• more expressive sequence modeling
• can capture more complex temporal patterns
• harder to train

So Deep RNNs extend the same idea.

They do not replace recurrence.

They deepen it.

Where RNNs Fit in Deep Learning

RNNs became important because different data types need different architectures.

CNNs work well for images because images have spatial structure.

RNNs work well for sequences because sequences have order.

CNN:

• local spatial patterns
• image-centered tasks
• convolution kernels

RNN:

• temporal or sequential patterns
• text, speech, time series
• recurrent hidden state

This is why RNNs became one of the major deep learning architectures.

They match the structure of sequential data.

Practical Limits

RNNs are powerful, but they have limits.

Long sequences are hard.

Information from early steps can weaken over time.

Training can become unstable because gradients must pass through many time steps.

This is one reason later architectures became important.

Attention mechanisms and Transformers changed the landscape by making long-range relationships easier to model.

But RNNs remain the best starting point for understanding sequence modeling.

Recommended Learning Order

If RNNs feel abstract, learn them in this order:

1. Neural Network
2. Recurrent Neural Network
3. Hidden State
4. Deep RNN
5. CNN vs RNN comparison
6. Attention Mechanism
7. Transformer

This order works because you first understand normal neural networks.

Then you see what changes when order matters.

Then you understand why modern sequence models moved beyond basic recurrence.

Takeaway

An RNN is a neural network designed for sequences.

The shortest version is:

RNN = current input + previous hidden state

It reads data step by step.

It carries context forward.

It uses that context to make better predictions on ordered data.

If you remember one idea, remember this:

RNNs make neural networks sequence-aware by passing hidden state from one time step to the next.

Discussion

When learning sequence models, do you find it easier to start from RNNs first, or jump directly to Attention and Transformers?

Originally published at zeromathai.com.
Original article: https://zeromathai.com/en/rnn-complete-hub-en/

GitHub Resources
AI diagrams, study notes, and visual guides:
https://github.com/zeromathai/zeromathai-ai