NAS (Neural Architecture Search)

Neural Architecture Search (NAS) is an automated process that optimizes deep learning model architectures. Instead of manually designing neural networks, NAS uses machine learning algorithms to identify the best network structures for specific tasks, enhancing performance while reducing computational costs.

Key Features:

• Automated model optimization – Finds the best architecture without human intervention.

• Performance-driven – Improves accuracy and efficiency.

• Reduces development time – Speeds up AI model training.

• Customizable – Tailors architectures to specific applications.

Best Use Cases:

• Computer vision models for image recognition.

• Natural language processing for text analysis.

• AI model efficiency improvements.

• Healthcare AI for medical imaging.