CS5720 - Week 7
Slide 125 of 140

LSTM vs Standard RNN: The Ultimate Comparison

Standard RNN
Simple but Limited
RNN
Cell
Single hidden state only
Memory Span
5-10 steps
Gradient Flow
Vanishing
Parameters
Low
Training
Difficult
LSTM
Complex but Powerful
LSTM
Cell
f
i
o
Cell state + Hidden state + Gates
Memory Span
100+ steps
Gradient Flow
Stable
Parameters
4x Higher
Training
Stable
📊 Performance Comparison
Long-term Memory
Standard RNN
25%
LSTM
90%
Gradient Stability
Standard RNN
30%
LSTM
85%
Sequence Length Handling
Standard RNN
35%
LSTM
95%
Computational Efficiency
Standard RNN
80%
LSTM
60%
🔍 Key Architectural Differences
State Management
RNN: Single hidden state handles everything
LSTM: Separate cell state and hidden state for specialized functions
Information Control
RNN: No selective information flow control
LSTM: Three gates control forget, input, and output operations
Memory Capability
RNN: Limited to recent information (5-10 steps)
LSTM: Can maintain information across hundreds of steps
Learning Dynamics
RNN: Suffers from vanishing/exploding gradients
LSTM: Stable gradient flow enables effective learning
Best Use Cases
RNN: Simple, short sequences with limited computational resources
LSTM: Complex, long sequences requiring sophisticated memory
Implementation Complexity
RNN: Simple implementation, easy to understand
LSTM: Complex architecture but well-supported in frameworks
← Previous Next →
Prepared by Dr. Gorkem Kar