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Algorithmic Data Curation in Machine Learning
Exploring the necessity and challenges of algorithmic data curation in handling the abundance of data for model training, focusing on filtering out semantic duplicates, redundancy, and bad data. The chapter discusses the shift from manual to algorithmic approaches, emphasizing the importance of evaluating data quality for optimal model performance.