Village Green Shopping Centre | Vernon's Premier Shopping Destination
Choice Made Simple!
Too many options?Click below to purchase an online gift card that can be used at participating retailers in Village Green Shopping Centre and continue your shopping IN CENTRE!Purchase Here
*Product information may vary - to confirm product availability, pricing, shipping and return information please contact Coles
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
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
Find everything in-store including new, used and children’s books, music, movies, games and toys. Visit Coles today to find the perfect gift, or a novel for yourself. COVID-19 UPDATE: Open | Regular Centre Hours
