Online Machine Learning

Name: Online Machine Learning
Brand: Springer, Berlin
SKU: 9789819970094
Price: 51.67 EUR
Availability: InStock

A Practical Guide with Examples in Python

Автор: Eva Bartz, Thomas Bartz-Beielstein

Език:

Корици: С меки корици

Издател: Springer, Berlin

Наличност: Външен склад

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51.67 € 101.05 лв

This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is d...

Информация за книгата

Автор

Eva Bartz, Thomas Bartz-Beielstein

Език

Английски език

Корици

Книга - С меки корици

Издадена

2025

страници

155

EAN

9789819970094

Enbook ID

47825947

Издател

Springer, Berlin

Теглоt

271

Размери

155 x 235

Пълно описание

This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications.

The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs.

OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice.

In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository (https://github.com/sn-code-inside/online-machine-learning). The repository is continuously maintained, so the notebooks may change over time.

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Online Machine Learning

A Practical Guide with Examples in Python

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