🔄 RNN & LSTM Basics
Master sequential data with recurrent networks
Your Progress
0 / 5 completed←
Previous Module
Word Embeddings Visualizer
Processing Sequences with Memory
Recurrent Neural Networks (RNNs) process sequential data by maintaining hidden state across time steps. Unlike feedforward networks, RNNs have loops that allow information to persist, making them ideal for text, time series, and any ordered data.
🎯 Why RNNs?
✓
Memory: Remember previous inputs
✓
Variable length: Handle any sequence size
✓
Temporal patterns: Capture time dependencies
✓
Shared weights: Same parameters each step
📝
Text Generation
Predict next character or word in sequence
📈
Time Series
Forecast stock prices, weather patterns
🌍
Translation
Convert sequences between languages
🔄 The Challenge
Vanilla RNN Problem
Suffers from vanishing/exploding gradients during training.
Hard to learn long-term dependencies
LSTM Solution
Uses gates to control information flow and memory.
Captures long-range patterns effectively