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Getting started with an ML project? Andrew Ng shares practical tips to set up a strong foundation—choosing a reasonable algorithm, running quick sanity checks, and focusing on data quality over chasing the latest models. These steps can save time, prevent errors, and make iteration more efficient. Streamline your workflow...

11,307 Aufrufe • vor 1 Jahr •via X (Twitter)

3 Kommentare

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Jonas ️vor 1 Jahr

Andrew Ng’s tips are spot on! Prioritizing data quality over chasing the latest models is key to efficient ML workflows. His courses are a must for any AI enthusiast! #aspie

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opensourceCMvor 1 Jahr

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DataInstavor 1 Jahr

solid advice! a strong foundation makes all the difference in ml projects!

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