Episode Synopsis "Patrick Riley (Season 2, Ep. 3)"

Our guest on this episode is Dr. Patrick Riley from Google Accelerated Science.Some relevant papers and links:Riley, P., Practical advice for analysis of large, complex data sets. The Unofficial Google Data Science Blog, www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html (2016)Zinkevich, M., Rules of Machine Learning: Best Practices for ML Engineering. https://developers.google.com/machine-learning/guides/rules-of-ml/ (last updated Oct 2018)Wigner, E., The Unreasonable Effectiveness of Mathematics in the Natural Sciences. Communications in Pure and Applied Mathematics, doi:10.1002/cpa.3160130102 (1960)Gulshan, V., Peng, L, Coram, M., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. The Journal of the American Medical Association, doi:10.1001/jama.2016.17216 (2016)Google Accelerated Science website: ai.google/research/teams/applied-science/gas

Listen "Patrick Riley (Season 2, Ep. 3)"

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• Paper Interview - Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
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• Turab Lookman (Season 2, Ep.4)
• Patrick Riley (Season 2, Ep. 3)
• O. Anatole von Lilienfeld (Season 2, Ep. 2)
• Gábor Csányi (Season 2, Ep. 1)
• Evan J. Reed (Season 1, Ep. 3)
• Ekin Dogus Cubuk (Season 1, Ep. 2)
• Kieron Burke (Season 1, Ep. 1)
• Introduction (Season 1, Ep. 0)