Hi Pythonistas!
In the last post we learned about CNNs, CNNs are great at looking.
But some problems aren’t about images. They’re about order.
- Words in a sentence
- Time series data
- Speech
- Stock prices
For these, sequence matters.
Enter Recurrent Neural Networks (RNNs).
Why Dense & CNNs Fall Short for Sequences
Dense layers:
See everything at once
Forget what came before
CNNs:
Capture local patterns
But struggle with long-range dependencies
In sequences:
“I am not happy”
That not changes everything
We need memory.
The Core Idea of RNNs
RNNs process data one step at a time.
At each step, they take:
- Current input
- Previous hidden state (memory)
- And produce:Output
- New hidden state
Same network, reused again and again.
That’s the recurrent part.
Think of It Like This
An RNN is like:
Reading a sentence word by word Keeping notes in your head Updating understanding as you go
You don’t reread the entire sentence every time you remember.
Why RNNs Are Powerful
- Handle variable-length inputs
- Capture temporal patterns
- Share parameters across time
That’s why RNNs were huge in:
- Language modeling
- Speech recognition
- Time-series forecasting
The Big Problem with RNNs
Remember Chapter 20?
Yep vanishing & exploding gradients.
In long sequences:
Gradients vanish → early words forgotten
Gradients explode → unstable training
RNNs forget long-term context very easily.
LSTM & GRU Smarter RNNs
To fix this, we got:
LSTM (Long Short-Term Memory)
GRU (Gated Recurrent Unit)
They introduce gates:
- What to remember
- What to forget
- What to pass forward
Think of them as RNNs with filters on memory.
RNNs in the Real World
You’ll find RNNs in:
- Text generation
- Machine translation
- Speech-to-text
- Time-series prediction
- The Transition to Transformers
RNNs taught us:
Sequences need memory
Order matters
But they’re:
Slow (sequential processing)
Hard to train on long sequences
This opened the door for:
Transformers (coming soon)
What I Learned This Week
RNNs handle sequential data
They reuse the same network across time
Memory comes from hidden states
Vanishing gradients limit long-term memory
LSTM & GRU improved things
RNNs were the first models that truly remembered and they paved the way for everything that came after.
What's next
You already know what is next ie Transformers