What is Neural Architecture Search?
Neural Architecture Search (NAS) is a cutting-edge technique in the field of automated machine learning (AutoML) that aims to automate the design of neural networks. Traditional neural network architecture design often relies on human expertise, which is a time-consuming process. NAS automates this by using search algorithms to explore and discover optimal neural network architectures for specific tasks. It involves defining a search space of possible architectures and then employing optimization methods, such as genetic algorithms or reinforcement learning, to find the most effective architecture.
NAS has shown promising results in outperforming manually designed networks in various tasks, including image recognition and natural language processing. The automated nature of NAS allows for more efficient and sophisticated neural network designs, ultimately pushing the boundaries of what is achievable in artificial intelligence and machine learning applications.
Neural Architecture Search Algorithm
Neural Architecture Search (NAS) falls within the realm of automated machine learning (AutoML). AutoML is a comprehensive term encompassing the automation of diverse tasks in the application of machine learning to real-world challenges. The article explores the fundamentals, and applications of the NAS algorithm.
Table of Content
- What is Neural Architecture Search?
- Components of Neural Architecture Search
- Neural Architecture Search and Transfer Learning
- Applications of Neural Architecture Search(NAS)
- Advantages and Disadvantages of Neural Architecture Search