Applications of Neural Architecture Search(NAS)
Neural Architecture Search (NAS) is a versatile method for optimizing neural network topologies, as evidenced by its applications in a wide range of areas. Among the important applications are:
- Computer Vision: Semantic segmentation, object detection, and image classification problems have all been designed using NAS. The process of identifying the best network architecture for visual recognition is automated.
- AutoML: NAS is a fundamental component of AutoML, contributing to the automation of the entire machine learning pipeline, from architecture design to hyperparameter tuning.
- Natural Language Processing (NLP): In NLP tasks like machine translation, sentiment analysis, and named entity recognition, NAS aids in developing customized architectures that capture intricate language patterns.
- Autonomous Vehicles: NAS contributes to developing neural network architectures for perception tasks in autonomous vehicles, such as object detection, lane tracking, and scene understanding.
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