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Ensemble Learning Developers Learn by Kumar Alok (12 results)
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Ensemble Learning for Ai Developers : Learn Bagging, Stacking, and Boosting Methods With Use Cases
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Add to basketPaperback. Condition: Brand New. 152 pages. 9.25x6.10x0.40 inches. In Stock.
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Ensemble Learning for Ai Developers : Learn Bagging, Stacking, and Boosting Methods With Use Cases
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Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases
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Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases
Language: English
Published by Apress, Incorporated, 2020
ISBN 10: 1484259394 ISBN 13: 9781484259399
Condition: New. pp. 155.
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Taschenbuch. Condition: Neu. Ensemble Learning for AI Developers | Learn Bagging, Stacking, and Boosting Methods with Use Cases | Alok Kumar (u. a.) | Taschenbuch | xvi | Englisch | 2020 | Apress | EAN 9781484259399 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases
Language: English
Published by Apress, Incorporated, 2020
ISBN 10: 1484259394 ISBN 13: 9781484259399
Condition: New. Print on Demand pp. 155.
-
Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases
Language: English
Published by Apress, Incorporated, 2020
ISBN 10: 1484259394 ISBN 13: 9781484259399
Condition: New. PRINT ON DEMAND pp. 155.
-
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Use ensemble learning techniques and models to improve your machine learning results.Ensemble Learning for AI Developersstarts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.What You Will LearnUnderstand the techniques and methods utilized in ensemble learningUse bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce biasEnhance your machine learning architecture with ensemble learningWho This Book Is ForData scientists and machine learning engineers keen on exploring ensemble learning.
