Authors:
- Presents an intuitive and comprehensive view of AI, with emphasis on theory and applications of Deep Learning.
- Mathematical notation is simplified and various illustrations are used to convey concepts in an intuitive manner, making this suitable for anyone who wants to learn the fundamentals of AI
- Emphasizes both the theoretical and applied sides of AI and therefore will be able to meet different learning needs
- Builds a high-level, unifying, insightful, bird's eye view of a plethora of techniques based on deep neural networks and their numerous variations for different applications
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Table of contents (17 chapters)
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Front Matter
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Applications
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Front Matter
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The Road Ahead
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Front Matter
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About this book
An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.
The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.
Authors and Affiliations
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Technology and Innovation Office, Varian Medical Systems, A Siemens Healthineers Company, Palo Alto, USA
Simant Dube
About the author
Bibliographic Information
Book Title: An Intuitive Exploration of Artificial Intelligence
Book Subtitle: Theory and Applications of Deep Learning
Authors: Simant Dube
DOI: https://doi.org/10.1007/978-3-030-68624-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-68623-9Published: 22 June 2021
Softcover ISBN: 978-3-030-68626-0Published: 23 June 2022
eBook ISBN: 978-3-030-68624-6Published: 21 June 2021
Edition Number: 1
Number of Pages: XXII, 352
Number of Illustrations: 4 b/w illustrations, 72 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Multiagent Systems, Pattern Recognition