Overview
- Explains ensemble learning with less math and more programming-friendly abstractions than presented in other books so it is easier for you to learn
- Discusses the competitive edge that you can achieve by using machine learning that includes ensemble techniques
- Covers the effective use of ensemble concepts and popular libraries such as Keras, Scikit Learn, TensorFlow, and PyTorch
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Table of contents (6 chapters)
Keywords
About this book
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
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Ensemble Learning for AI Developers
Book Subtitle: Learn Bagging, Stacking, and Boosting Methods with Use Cases
Authors: Alok Kumar, Mayank Jain
DOI: https://doi.org/10.1007/978-1-4842-5940-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Alok Kumar and Mayank Jain 2020
Softcover ISBN: 978-1-4842-5939-9Published: 19 June 2020
eBook ISBN: 978-1-4842-5940-5Published: 18 June 2020
Edition Number: 1
Number of Pages: XVI, 136
Number of Illustrations: 51 b/w illustrations
Topics: Artificial Intelligence, Python, Open Source