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Titlebook: Ensemble Learning for AI Developers; Learn Bagging, Stack Alok Kumar,Mayank Jain Book 2020 Alok Kumar and Mayank Jain 2020 Ensemble Learnin

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發(fā)表于 2025-3-21 16:29:20 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Ensemble Learning for AI Developers
副標題Learn Bagging, Stack
編輯Alok Kumar,Mayank Jain
視頻videohttp://file.papertrans.cn/312/311369/311369.mp4
概述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
圖書封面Titlebook: Ensemble Learning for AI Developers; Learn Bagging, Stack Alok Kumar,Mayank Jain Book 2020 Alok Kumar and Mayank Jain 2020 Ensemble Learnin
描述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
出版日期Book 2020
關(guān)鍵詞Ensemble Learning; Machine Learning; Regression; Supervised Learning; Artificial Intelligence; Python; Dee
版次1
doihttps://doi.org/10.1007/978-1-4842-5940-5
isbn_softcover978-1-4842-5939-9
isbn_ebook978-1-4842-5940-5
copyrightAlok Kumar and Mayank Jain 2020
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發(fā)表于 2025-3-21 21:37:13 | 只看該作者
Book 2020 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
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發(fā)表于 2025-3-22 03:12:44 | 只看該作者
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發(fā)表于 2025-3-22 04:37:33 | 只看該作者
http://image.papertrans.cn/e/image/311369.jpg
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發(fā)表于 2025-3-22 11:39:38 | 只看該作者
https://doi.org/10.1007/978-1-4842-5940-5Ensemble Learning; Machine Learning; Regression; Supervised Learning; Artificial Intelligence; Python; Dee
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發(fā)表于 2025-3-22 16:38:01 | 只看該作者
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發(fā)表于 2025-3-22 21:17:12 | 只看該作者
Why Ensemble Techniques Are Needed, context of musicians who regularly play together. An ensemble of musicians is the sum of individual compositions by multiple musicians. Similarly, in machine learning, . is a combination of multiple machine learning techniques performed together.
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發(fā)表于 2025-3-23 02:47:05 | 只看該作者
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發(fā)表于 2025-3-23 09:03:25 | 只看該作者
Gillian McCann,Gitte BechsgaardIn Chapter ., you learned how to divide and mix training data in different ways to build ensemble models, which perform better than a model that was trained on an undivided dataset.
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