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(as of Dec 25,2024 13:10:04 UTC – Details)
ASIN : B07S3MLGFW
Publisher : Springer; 1st ed. 2019 edition (May 17, 2019)
Publication date : May 17, 2019
Language : English
File size : 16669 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 396 pages
Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning)
In the world of machine learning, the concept of automation has become increasingly popular and necessary. With the exponential growth of data and the complexity of machine learning models, automating the process of building and optimizing models has become essential for businesses and researchers alike. This is where Automated Machine Learning (AutoML) comes into play.
The Springer Series on Challenges in Machine Learning explores the various methods, systems, and challenges associated with AutoML. This series delves into the cutting-edge research and advancements in the field, providing insights into how AutoML is revolutionizing the way machine learning models are built and deployed.
From automated feature engineering and model selection to hyperparameter tuning and model interpretation, the series covers a wide range of topics related to AutoML. Researchers and practitioners in the field will find valuable information on the latest techniques and tools for automating the machine learning process.
However, with these advancements come challenges. The series also explores the limitations and potential pitfalls of AutoML, such as overfitting, bias, and interpretability issues. By addressing these challenges, researchers can work towards developing more robust and reliable automated machine learning systems.
Overall, the Springer Series on Challenges in Machine Learning is a must-read for anyone interested in the future of machine learning and the role of automation in driving innovation in the field. Stay tuned for the latest research and insights on Automated Machine Learning.
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