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Practical Machine Learning – A New Look at Anomaly Detection
Ted Dunning
€ 23.99
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Description for Practical Machine Learning – A New Look at Anomaly Detection
Paperback. This O'Reilly report uses practical example to explain how the underlying concepts of anomaly detection work. Num Pages: 66 pages, colour illustrations. BIC Classification: UY. Category: (XV) Technical / Manuals. Dimension: 152 x 220 x 4. Weight in Grams: 112.
Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what "suspects" you're looking for. This O'Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts ... Read more
Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what "suspects" you're looking for. This O'Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts ... Read more
Product Details
Publisher
O´Reilly Media
Format
Paperback
Publication date
2014
Condition
New
Weight
111g
Number of Pages
66
Place of Publication
Sebastopol, United States
ISBN
9781491911600
SKU
V9781491911600
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1
About Ted Dunning
Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for these Apache projects: Spark, Storm, Stratosphere, and Datafu. He contributed to Mahout clustering, classification, and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect ... Read more
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