CS5720 - Week 12
Slide 223 of 240

Automated Hyperparameter Optimization

Why Automate Hyperparameter Tuning?

AutoML Benefits
  • ⏱️ Saves countless hours of manual tuning
  • 🎯 Finds better hyperparameters than humans
  • 🔄 Systematically explores parameter space
  • 📊 Provides insights into parameter importance
  • 🚀 Scales to complex, high-dimensional spaces
Key Advantages:

• Reproducible results with systematic search
• Parallel execution across multiple GPUs/machines
• Early stopping to avoid wasting compute
• Visualization of optimization progress

Popular AutoML Tools

Optuna
Modern, distributed hyperparameter optimization framework with pruning and visualization.
✓ Bayesian optimization • Pruning • Distributed search
Hyperopt
Established library using Tree-structured Parzen Estimators (TPE) for optimization.
✓ TPE algorithm • MongoDB integration • Mature ecosystem
AutoKeras
Automated machine learning for deep learning, automatically designs architectures.
✓ Neural Architecture Search • Easy API • Keras integration
Auto-sklearn
Automated machine learning toolkit built on scikit-learn with ensemble methods.
✓ Algorithm selection • Ensemble building • Meta-learning

Manual vs Automated Hyperparameter Tuning

👨‍💻
Manual Tuning
Traditional human-driven approach
3-5
Days to tune
20-50
Trials explored
85%
Typical accuracy
High
Human effort
🤖
Automated Tuning
AI-powered optimization algorithms
4-8
Hours to tune
100-500
Trials explored
92%
Typical accuracy
Low
Human effort
Click on any tool or approach to learn more about implementation details!
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Prepared by Dr. Gorkem Kar