Production DL
NAS — Let AI Design Its Own Neural Networks
Neural Architecture Search replaces manual engineering with algorithmic search, discovering architectures that outperform human-designed networks. From DARTS to EfficientNet, NAS finds optimal depth, width, and connectivity patterns for any task and hardware constraint.
- Key point 1 — DARTS enables differentiable architecture search with gradient-based optimization
- Key point 2 — EfficientNet's compound scaling jointly optimizes depth, width, and resolution
- Key point 3 — Once-for-All networks train one supernetwork to deploy many subnets
"The future of architecture is architecture that designs itself."
Neural Architecture Search
NAS automates the design of neural network architectures, replacing manual engineering with algorithmic search. It has discovered architectures that outperform human-designed networks.
NAS Framework
Search Space
DARTS (Differentiable Architecture Search)
EfficientNet
Once-for-All (OFA)
Search Strategies
PyTorch Implementation
Practical Considerations
Practice Exercises
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DARTS on CIFAR-10: Implement and run DARTS search. Visualize the discovered architecture.
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Compound scaling: Reproduce EfficientNet scaling experiments. Plot accuracy vs. FLOPs.
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Hardware-aware NAS: Add latency objective to DARTS. Find Pareto-optimal architectures.
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Once-for-All: Train OFA supernetwork on MNIST. Extract subnets for different FLOP budgets.
Key Takeaways
What to Learn Next
-> Model Compression Make deep learning models fast and efficient for production deployment.
-> DL Systems Design Master distributed training, monitoring, and production deployment of deep learning models.
-> Self-Supervised Learning Learn useful representations from unlabeled data without manual annotation.
-> CNN Architecture Deep Dive Master convolutional layers, pooling, and modern CNN architectures.
-> Attention Mechanisms Discover how attention solves the information bottleneck in sequence models.
-> Vision Transformers Apply Transformer architecture to image recognition by treating patches as tokens.