Recurrent Neural Networks & LSTMs

A feedforward network sees each input in isolation — it has no memory. An RNN feeds its own output back as input at the next time step, so the hidden state carries information forward through a sequence. Same set of weights applied recursively. That single trick — weight sharing across time — let neural networks model language, speech, and stock prices for two decades, until transformers ate the world.

Learning Objectives

After this lesson, you will be able to:

See how looping connections give neural networks a form of memory for processing sequences
Understand the vanishing gradient problem -- why vanilla RNNs forget things that happened long ago
Follow data through LSTM gates and see how the cell state acts as a long-term memory highway
Explain the role of each LSTM gate (forget, input, output) and why the cell state update avoids the vanishing gradient that plagues vanilla RNNs

Prerequisites

Before You Start

Review

Why

Why This Matters

How your keyboard predicts your next word -- when you type "I am on my" and your phone suggests "way," that is a sequence model carrying memory of what you have typed so far; many phone keyboards still use RNNs because they are small enough to run on-device
Why transformers had to be invented -- RNNs process words one at a time and their memory fades over long sequences; by the end of a paragraph, the model has forgotten the beginning; this weakness motivated the attention mechanism and the transformer revolution
How smartwatch apps detect irregular heartbeats -- LSTMs process heart rate data as a time series, maintaining memory of the normal rhythm so they can flag the moment something looks off
Build this --> Build a text predictor: train a character-level RNN on a corpus of text (like your favorite book or your own messages), then type a few characters and watch the model generate text in that style, one character at a time -- see it capture patterns like common word endings and punctuation habits

Recurrent Neural Networks & LSTMs

After this lesson, you will be able to:

Before You Start

End-to-End Sequence Processing

Your Reflection

Continue Learning

Discussion

#Step 1: Input Word "The"

#Step 2: Embed to Vector

#Step 3: Hidden State h0

#Step 4: Next Word "cat"

#Step 5: Update Hidden State

#Step 6: Continue Sequence

#Step 7: Output at Each Step

#The Need for Memory

#The Vanilla RNN

#The Vanishing Gradient Problem in RNNs

#LSTM: The Long Short-Term Memory

#The Cell State: An Information Highway

#The Forget Gate: What to Erase

#The Input Gate: What to Write

#Update the Cell State

#The Output Gate: What to Reveal

#Why Gradients Survive

#The LSTM Equations

#GRU: The Simplified Alternative

#Try It Yourself

Deep Dive

#Key Takeaways

#Quick Check

Related Lessons