Ensemble Learning for AI Developers
Learn Bagging, Stacking, and Boosting Methods with Use Cases
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Beschreibung
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.
You will:
- 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
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
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
Autor*in
Alok Kumar
Themen in »Ensemble Learning for AI Developers«
Ensemble Learning Machine Learning Regression Supervised Learning Artificial Intelligence Python Deep Learning Neural Networks NumPy SciPy