Picture 1 of 2

Stock photo

Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting - Picture 1 of 2

Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting - Picture 2 of 2

Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases by Mayank Jain, Alok Kumar (Paperback, 2020)

loveourprices2 (118579)

96% positive Feedback

Price:

£29.07

Free postage

Estimated delivery Wed, 30 Jul - Thu, 14 Aug

Returns:

30 days return. Buyer pays for return postage. If you use an eBay delivery label, it will be deducted from your refund amount.

Condition:

New

¿ Use of bagging or bootstrap aggregating for making ensemble model. ¿ Boosting : We will talk in detail about various boosting techniques with historical. ¿ Stacking : We will talk in detail about various stacking techniques are used in machine.

About this product

Product Information

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