Navigation überspringen
Universitätsbibliothek Heidelberg
Standort: ---
Exemplare: ---
 Online-Ressource
Verfasst von:Kumar, Alok [VerfasserIn]   i
 Jain, Mayank [VerfasserIn]   i
Titel:Ensemble Learning for AI Developers
Titelzusatz:Learn Bagging, Stacking, and Boosting Methods with Use Cases
Institutionen:Safari, an O'Reilly Media Company. [MitwirkendeR]   i
Verf.angabe:Kumar, Alok
Ausgabe:1st edition
Verlagsort:[Erscheinungsort nicht ermittelbar]
Verlag:Apress
Jahr:2020
Umfang:1 online resource (146 pages)
Fussnoten:Online resource; Title from title page (viewed June 18, 2020)
ISBN:978-1-4842-5940-5
Abstract:Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts 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 Learn Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning Who This Book Is For Data scientists and machine learning engineers keen on exploring ensemble learning
ComputerInfo:Mode of access: World Wide Web.
URL:Aggregator: https://learning.oreilly.com/library/view/-/9781484259405/?ar
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Electronic books ; local
 Electronic books
K10plus-PPN:1702679098
 
 
Lokale URL UB: Zum Volltext
 
 Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
 Klinikum MA Bestellen/Vormerken für Benutzer des Klinikums Mannheim
Eigene Kennung erforderlich
Bibliothek/Idn:UW / m3691343929
Lokale URL Inst.: Zum Volltext

Permanenter Link auf diesen Titel (bookmarkfähig):  https://katalog.ub.uni-heidelberg.de/titel/68592673   QR-Code
zum Seitenanfang