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1. scholar.google.com › citations‪Andrew Ng‬ - ‪Google Scholar‬

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   Andrew Ng. Stanford University. Verified email at cs.stanford.edu - Homepage. Machine Learning Deep Learning AI. Title. Sort. Sort by citations Sort by year Sort by title. Cited by.

2. scholar.google.com › citations‪Andrew Ng‬ - ‪Google Scholar‬

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   16. 2022. Articles 1–20. ‪Pyrogen Group‬ - ‪‪Cited by 1,396‬‬ - ‪Pyrotechnics‬ - ‪Surfactants‬ - ‪Colloids‬ - ‪Battery Safety‬ - ‪Thermal Runaway‬.

3. en.wikipedia.org › wiki › Andrew_NgAndrew Ng - Wikipedia

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   Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a British-American computer scientist and technology entrepreneur focusing on machine learning and artificial intelligence (AI). Ng was a cofounder and head of Google Brain and was the former Chief Scientist at Baidu , building the company's Artificial Intelligence Group into a team of ...

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4. www.andrewng.orgAndrew Ng

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   Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He is Founder of DeepLearning.AI , Founder & CEO of Landing AI , General Partner at AI Fund , Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University’s Computer Science Department.

5. www.coursera.org › instructor › andrewngAndrew Ng, Instructor | Coursera

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   Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University.

6. research.google › pubs › building-high-level-features-usingBuilding high-level features using large scale unsupervised ...

   research.google › pubs › building-high-level-features-using

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   Andrew Ng. International Conference in Machine Learning (2012) Download Google Scholar. Copy Bibtex. Abstract. We consider the problem of building highlevel, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images?

7. www.andrewng.org › publications › algorithms-forAlgorithms for inverse reinforcement learning - Andrew Ng

   www.andrewng.org › publications › algorithms-for

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   Abstract. This paper addresses the problem of inverse reinforcement learning (IRL) in Markov decision processes, that is, the problem of extracting a reward function given observed, optimal behavior.