Term
Neural Architecture Search (NAS)
Definition
Neural Architecture Search (NAS) is a method in artificial intelligence that automatically finds the most effective neural network structure for a specific task. It aims to improve the performance of AI models by optimizing their design without human intervention.
Where you’ll find it
NAS is typically found within AI development environments or platforms that support advanced machine learning capabilities. It may not be available on all platforms, so it's worth checking if your specific AI tool or software supports NAS functionality.
Common use cases
- Optimizing AI models to improve performance on specific tasks like image recognition or natural language processing.
- Reducing the time and expertise required to manually design effective neural networks.
- Experimenting with different model architectures to identify the best option for a given data set.
Things to watch out for
- NAS can be very computationally expensive, requiring significant processing power and potentially increasing costs.
- The process can be time-consuming, as it explores numerous architectural options.
- It may not always provide a better result than a well-tuned traditional model, depending on the complexity of the task.
Related terms
- Machine Learning
- Model Optimization
- Hyperparameter Tuning
- Deep Learning
- Transfer Learning