Sequential Models: RNNs
and LSTMs
This lecture covers Recurrent Neural Networks (RNNs) and Long ShortTerm Memory networks (LSTMs). We will explore theory, applications, and
PyTorch implementations.
by snigemigmatic
Introduction to Sequential Models
Sequential data is everywhere: time series, text, speech, gene sequences.
Traditional neural networks assume independent data.
RNNs are designed for sequential data, capturing temporal dependencies.
RNNs incorporate loops, creating memory to learn from previous inputs.
Why We Need Sequential Models
Traditional networks have limitations:
Fixed input/output dimensions
No parameter sharing
Inability to model temporal dependencies
RNNs address these limitations by:
Processing sequences element by element
Sharing parameters across positions
Maintaining an internal state
Use Cases and Advantages
Sequential models excel where temporal relationships are crucial.
Key capabilities:
Variable-length processing
Temporal pattern recognition
Context-aware processing
Memory-based prediction
Real-World Applications
Natural Language Processing
Text generation
Machine translation
Sentiment analysis
Question answering
Speech and Audio Processing
Speech recognition
Voice synthesis
Music generation
Time Series Analysis
Stock price forecasting
Weather prediction
Anomaly detection
Computer Vision (with CNNs)
Video analysis
Action recognition
Visual search
RNN Structure and Propagation
Architectural Components:
Input layer
Hidden/Recurrent layer
Output layer
Unfolding the RNN:
Deep feedforward network over time
Parameters shared across time steps
Trained using backpropagation through time (BPTT)
Issues with RNNs
Vanishing Gradient Problem:
Gradients become extremely small
Multiplication with small values
Difficult to learn long-term dependencies
Exploding Gradient Problem:
Gradients grow exponentially
Unstable training process
Solution: gradient clipping
Long Short-Term Memory (LSTM) Networks
LSTMs address the vanishing gradient problem.
Key innovation: sophisticated memory cell.
LSTM Architecture:
Cell state
Forget Gate
Input Gate
Output Gate
Gating mechanisms allow selective remembering/forgetting.