CS5720 - Week 6
Slide 119 of 120

RNN Training Challenges

Common Challenges

Training RNNs is notoriously difficult! They suffer from unique challenges due to their recurrent nature and long-term dependencies.
  • 📉
    Vanishing Gradients
    Gradients become exponentially small
  • 💥
    Exploding Gradients
    Gradients grow exponentially large
  • 🧠
    Long-Term Dependencies
    Difficulty learning distant relationships
  • ⏱️
    Computational Cost
    Sequential nature prevents parallelization

Solutions & Techniques

✂️
Gradient Clipping
Limit gradient magnitude
🏗️
Better Architectures
LSTM, GRU, Attention
🎯
Smart Initialization
Proper weight initialization
🔧
Training Techniques
TBPTT, curriculum learning
Remember:
Don't give up! These challenges have well-established solutions. Start simple and gradually add complexity.

Gradient Flow Visualization

Vanishing Gradients
10⁻⁸
Gradients too small to learn
Healthy Gradients
10⁻³
Optimal learning range
Exploding Gradients
10⁸
Gradients cause instability
Click on scenarios to understand gradient flow problems and their solutions
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Prepared by Dr. Gorkem Kar