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Ensemble learning for AI developers [electronic resource] : learn bagging, stacking, and boosting methods with use cases

Ensemble learning for AI developers [electronic resource] : learn bagging, stacking, and boosting methods with use cases

자료유형
단행본
개인저자
Kumar, Alok. Jain, Mayank.
서명 / 저자사항
Ensemble learning for AI developers [electronic resource] : learn bagging, stacking, and boosting methods with use cases / Alok Kumar, Mayank Jain.
발행사항
[United States] : Apress, 2020.
형태사항
xvi, 136 p. : ill. ; 24 cm.
ISBN
9781484259399
일반주기
Includes index.
일반주제명
Artificial intelligence. Python (Computer program language). Open source software. Computer programming.
000 00000nam u2200205 a 4500
001 000046063310
005 20210113113538
008 210111s2020 xxua 0 001 0 eng d
020 ▼a 9781484259399
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.3 ▼2 23
084 ▼a 006.3 ▼2 DDCK
090 ▼a 006.3 ▼b K962e
100 1 ▼a Kumar, Alok.
245 1 0 ▼a Ensemble learning for AI developers ▼h [electronic resource] : ▼b learn bagging, stacking, and boosting methods with use cases / ▼c Alok Kumar, Mayank Jain.
260 ▼a [United States] : ▼b Apress, ▼c 2020.
300 ▼a xvi, 136 p. : ▼b ill. ; ▼c 24 cm.
500 ▼a Includes index.
650 0 ▼a Artificial intelligence.
650 0 ▼a Python (Computer program language).
650 0 ▼a Open source software.
650 0 ▼a Computer programming.
700 1 ▼a Jain, Mayank.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.3 K962e 등록번호 121256050 도서상태 대출중 반납예정일 2021-03-25 예약 예약가능 R 서비스 M

컨텐츠정보

저자소개

Alok Kumar(지은이)

Mayank Jain(지은이)

정보제공 : Aladin

목차

Chapter 1: An Introduction to Ensemble LearningChapter Goal: This chapter will give you a brief overview of ensemble learningNo of pages - 10Sub -Topics Need for ensemble techniques in machine learning Historical overview of ensemble learning A brief overview of various ensemble techniques Chapter 2: Varying Training DataChapter Goal: In this chapter we will talk in detail about ensemble techniques where trainingdata is changed.No of pages: 30Sub - Topics: Use of bagging or bootstrap aggregating for making ensemble model Code samples Popular libraries support for bagging and best practices Introduction to random forests models Hands-on code examples for using random forest models Introduction to cross validation methods in machine learning Intro to K-Fold cross validation ensembles with code samples Other examples of varying data ensemble techniques
Chapter 3: Varying CombinationsChapter Goal : In this chapter we will talk about in detail about techniques where models areused in combination with one another to getting an ensemble learning boost.No of pages: 40Sub - Topics: Boosting : We will talk in detail about various boosting techniques with historical examples Introduction to adaboost , with code examples , Industry best practices and useful state of the art libraries for adaboost Introduction to gradient boosting , with hands on code examples with useful libraries and industry best practices for gradient boosting Introduction to XGboost with hands on code examples with useful libraries and industry best practices for XGboost Stacking : We will talk in detail about various stacking techniques are used in machine learning world Stacking in practice: How stacking is used by Kagglers for improving for winning entries.
Chapter 4: Varying ModelsChapter Goal: In this chapter we will talk about how ensemble learning models couldlead to better performance of your machine learning projectNo of pages: 30Sub - Topics: Training multiple model ensembles with code examples Hyperparameter tuning ensembles with code examples Horizontal voting ensembles Snapshot ensembles and its variants, Introduction to the cyclic learning rate. Code examples Use of ensembles in the deep learning world.
Chapter 5: Ensemble Learning Libraries and How to Use ThemChapter Goal: In this chapter we will go into details about some very popular libraries used bydata science practitioners and Kagglers for ensemble learningNo of pages: 25Sub - Topics: Ensembles in Scikit-Learn Learning how to use ensembles in TensorFlow Implementing and using ensembles in PyTorch Using Boosting using Microsoft LightGBM Boosting using XGBoost Stacking using H2O library Ensembles in R
Chapter 6: Tips and Best PracticesChapter Goal: In this chapter we will learn what are the best practices around ensemble learning with real world examplesNo of pages: 25Sub - Topics: How to build a state of the art Image classifier using ensembles How to use ensembles in NLP with real-world examples Use of ensembles for structured data analysis Using ensembles for time series data Useful tips and pitfalls How to leverage ensemble learning in Kaggle competitions Useful examples and case studies
Chapter 7 : The Path ForwardChapter goal - In this section we will cover recent advances in ensemble learningNo of pages: 10Sub - Topics: Recent trends and research in ensembles Use of ensembles in memory-constrained environments Use of ensembles in keeping eye of efficiency Useful resources

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