Ensemble Learning for AI Developers
Learn Bagging, Stacking, and Boosting Methods with Use Cases
(Sprache: Englisch)
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...
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...
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Klappentext zu „Ensemble Learning for AI Developers “
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
Inhaltsverzeichnis zu „Ensemble Learning for AI Developers “
Chapter 1: Why Ensemble Techniques Are Needed.- Chapter 2: Mix Training Data.- Chapter 3: Mix Models.- Chapter 4: Mix Combinations.- Chapter 5: Use Ensemble Learning Libraries.- Chapter 6: Tips and Best Practices.Autoren-Porträt von Alok Kumar, Mayank Jain
Alok Kumar is an AI practitioner and innovation lead at Publicis Sapient. He has extensiveexperience 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.
Bibliographische Angaben
- Autoren: Alok Kumar , Mayank Jain
- 2020, 1st ed., XVI, 136 Seiten, Maße: 15,5 x 1,1 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1484259394
- ISBN-13: 9781484259399
Sprache:
Englisch
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