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Data Collection Strategies for ML by Amirhossein Shokrani is a document available to read on EtoBox.
What is Data Collection Strategies for ML about?
The document discusses the critical importance of data collection in machine learning, highlighting the various stages involved such as data acquisition, labeling, and improvement of existing data. It also covers the Design of Experiments (DOE) as a method for data generation in engineering, detailing different DOE methods and their applications. Additionally, it explains the use of fractional factorial designs to efficiently study main effects and interactions while minimizing experimental runs.
- Author
- Amirhossein Shokrani
- Language
- EN