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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Index
Numbers
12 principles of agile methodology, 11–14
percent confidence interval, 20, 107
A
A/B replication, 229–230, 240–241
access, RAM (random access memory), 205–206
aggregation, 214
agile development, product focus and, 10–11
agile methodology, 12
algorithms
Cannon’s algorithm, 97
classification algorithms, 117
clustering algorithms, 117
k-means. see k-means
comparison algorithms. See comparison algorithms
ANNoy, 133
API buffering, queues, 243
application-level caching, 236
data ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.