Willkommen, schön sind Sie da!
Logo Ex Libris

Ensemble Learning for AI Developers

  • Kartonierter Einband
  • 152 Seiten
(0) Erste Bewertung abgeben
Bewertungen
(0)
(0)
(0)
(0)
(0)
Alle Bewertungen ansehen
Beginning-Intermediate user levelUse ensemble learning techniques and models to improve your machine learning results. Ensemble Le... Weiterlesen
CHF 49.50
Print on demand - Exemplar wird für Sie besorgt.
Bestellung & Lieferung in eine Filiale möglich

Beschreibung

Beginning-Intermediate user level

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


Autorentext

Alok Kumar is an AI practitioner and innovation lead at Publicis Sapient. He has extensive

experience in leading strategic initiatives and driving cutting-edge, fast-paced innovations. He won several awards and he is passionate about democratizing AI knowledge. He manages multiple non- profit learning and creative groups in NCR.

Mayank Jain currently works as Manager Technology at the Publicis Sapient Innovation Lab Kepler as an AI/ML expert. He has more than 10 years of industry experience working on cutting-edge projects to make computers see and think using techniques such as deep learning, machine learning, and computer vision. He has written several international publications, holds patents in his name, and has been awarded multiple times for his contributions.



Inhalt

Chapter 1: An Introduction to Ensemble Learning

Chapter Goal: This chapter will give you a brief overview of ensemble learning
No of pages - 10
Sub -Topics
Need for ensemble techniques in machine learning
Historical overview of ensemble learning
A brief overview of various ensemble techniques
Chapter 2: Varying Training Data
Chapter Goal: In this chapter we will talk in detail about ensemble techniques where training
data is changed.
No of pages: 30
Sub - 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 Combinations
Chapter Goal : In this chapter we will talk about in detail about techniques where models are
used in combination with one another to getting an ensemble learning boost.
No of pages: 40
Sub - 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 Models
Chapter Goal: In this chapter we will talk about how ensemble learning models could
lead to better performance of your machine learning project
No of pages: 30
Sub - 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 Them
Chapter Goal: In this chapter we will go into details about some very popular libraries used by
data science practitioners and Kagglers for ensemble learning
No of pages: 25
Sub - 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 Practices
Chapter Goal: In this chapter we will learn what are the best practices around ensemble learning with real world examples
No of pages: 25
Sub - 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 Forward
Chapter goal - In this section we will cover recent advances in ensemble learning
No of pages: 10
Sub - Topics:
Recent trends and research in ensembles
Use of ensembles in memory-constrained environments
Use of ensembles in keeping eye of efficiency
Useful resources

Produktinformationen

Titel: Ensemble Learning for AI Developers
Untertitel: Learn Bagging, Stacking, and Boosting Methods with Use Cases
Autor:
EAN: 9781484259399
ISBN: 1484259394
Format: Kartonierter Einband
Herausgeber: Apress
Genre: Informatik
Anzahl Seiten: 152
Gewicht: 242g
Größe: H235mm x B155mm x T8mm
Jahr: 2020
Auflage: 1st ed