🔍 Neural Architecture Search
Automated discovery of optimal neural network architectures
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0 / 5 completedIntroduction to NAS
🎯 What is Neural Architecture Search?
Neural Architecture Search (NAS) automates the design of neural networks by systematically exploring architecture configurations to find optimal structures for specific tasks.
Discover architectures that outperform human-designed networks
🌟 Why Use NAS?
Better Performance
Find architectures optimized for specific datasets and tasks
Design Automation
Reduce manual engineering and expert knowledge required
Efficiency Trade-offs
Balance accuracy, speed, and model size automatically
Novel Discoveries
Uncover unexpected architecture patterns and connections
🔧 NAS Components
Search Space
Defines possible architectures (layers, operations, connections)
Search Strategy
Algorithm to explore space (RL, evolution, gradient-based)
Performance Estimation
Evaluate candidate architectures (train, proxy metrics)
🏆 Landmark Discoveries
NASNet (2017)
RL-basedFirst NAS to outperform human designs on ImageNet
EfficientNet (2019)
Compound ScalingNAS + scaling achieves SOTA with fewer parameters
DARTS (2019)
Gradient-basedDifferentiable search reduces time from days to hours
⚖️ Benefits vs Challenges
Benefits
- • SOTA performance on benchmarks
- • Reduced human expertise needed
- • Task-specific optimization
- • Transferable architectures
Challenges
- • High computational cost
- • Large search spaces
- • Overfitting to search data
- • Reproducibility issues