Overview
- Illustrates how AI can be used to solve real-world problems within enterprise settings
- Deliberately written in such a way that makes it accessible to everyone regardless of their experience
- Provides a clear journey through the developing history of AI, especially in many enterprise applications
Part of the book series: Computational Intelligence Methods and Applications (CIMA)
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Table of contents (14 chapters)
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Front Matter
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Introduction and Overview
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Front Matter
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Foundations of Machine Learning
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Front Matter
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Deep Learning Concepts and Techniques
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Front Matter
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Enterprise Machine Learning
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Front Matter
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Keywords
About this book
This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications.
Authors and Affiliations
About the authors
Prof. Paul Fergus is a Professor in Machine Learning and Dr. Carl Chambers is a Senior Lecturer in the Dept. of Computer Science of Liverpool John Moores University. Their teaching responsibilities include Machine Learning and Data Science. Their research interest includes Applied Machine Learning, Computer Vision, Signal Processing, and Pattern Recognition.
Bibliographic Information
Book Title: Applied Deep Learning
Book Subtitle: Tools, Techniques, and Implementation
Authors: Paul Fergus, Carl Chalmers
Series Title: Computational Intelligence Methods and Applications
DOI: https://doi.org/10.1007/978-3-031-04420-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-04419-9Published: 19 July 2022
Softcover ISBN: 978-3-031-04422-9Published: 20 July 2023
eBook ISBN: 978-3-031-04420-5Published: 18 July 2022
Series ISSN: 2510-1765
Series E-ISSN: 2510-1773
Edition Number: 1
Number of Pages: XXVII, 341
Number of Illustrations: 1 b/w illustrations
Topics: Artificial Intelligence