May 2019
Beginner to intermediate
288 pages
9h 1m
English
book
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
by Andrew Kelleher, Adam Kelleher
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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Bibliography
[1] M. Herman, S. Rivera, S. Mills, J. Sullivan, P. Guerra, A. Cosmas, D. Farris, E. Kohlwey, P. Yacci, B. Keller, A. Kherlopian, and M. Kim, The Field Guide to Data Science. McLean, VA: Booz Allen, Nov. 2013.
[2] M. F. Smith, Software Prototyping: Adoption, Practice and Management. New York, NY: McGraw-Hill, Inc., 1991.
[3] Agile Alliance, “12 Principles Behind the Agile Manifesto,” Nov. 2015. https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/
[4] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
[5] R. Kohavi, R. Longbotham, D. Sommerfield, and R. M. Henne, “Controlled Experiments on the Web: Survey and Practical Guide,” Data Min. Knowl. Discov., vol. 18, no. ...
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