Neural Architecture Search
📰 Lilian Weng's Blog
Neural Architecture Search automates network architecture engineering to achieve best performance on a task
Action Steps
- Define the search space for potential network architectures
- Choose a search algorithm to explore the search space
- Select a child model evolution strategy to evaluate and refine architectures
Who Needs to Know This
ML researchers and engineers benefit from NAS as it automates the process of designing optimal network architectures, freeing up time for more strategic tasks
Key Insight
💡 NAS can significantly reduce the time and effort required to design optimal network architectures
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🤖 Automate network architecture engineering with Neural Architecture Search!
Key Takeaways
Neural Architecture Search automates network architecture engineering to achieve best performance on a task
Full Article
<!-- Neural Architecture Search (NAS) automates network architecture engineering. It aims to learn a network topology that can achieve best performance on a certain task. By dissecting the methods for NAS into three components: search space, search algorithm and child model evolution strategy, this post reviews many interesting ideas for better, faster and more cost-efficient automatic neural architecture search. --> <p>Although most popular and successful model architectures are designed by hum
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