Paul Fergus - Applied Deep Learning: Tools, Techniques, and Implementation (Computational Intelligence Methods and Applications)
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- Book:Applied Deep Learning: Tools, Techniques, and Implementation (Computational Intelligence Methods and Applications)
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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.
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The monographs and textbooks in this series explain methods developed in computational intelligence (including evolutionary computing, neural networks, and fuzzy systems), soft computing, statistics, and artificial intelligence, and their applications in domains such as heuristics and optimization; bioinformatics, computational biology, and biomedical engineering; image and signal processing, VLSI, and embedded system design; network design; process engineering; social networking; and data mining.
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Applied Artificial Intelligence: Mastering the Fundamentals, is aimed at students, academics and industry practitioners to provide them with a conceptual overview of the field. Students can use this book to supplement undergraduate, postgraduate and doctoral studies. Academics who are new to the area can utilise this book to gain a broad understanding of Artificial Intelligence while seasoned academics can use the book as a point of reference. In industry, managers will find this book useful for gaining an understanding of Artificial Intelligence and where it could be integrated into existing business processes. For those primally focused on development and implementation, the book along with its references provides a strong foundation for anyone moving into Artificial Intelligence development. The book discusses key frameworks such as TensorFlow, Dask, RAPIDS, Docker and Kubernetes. What makes this book accessible to a broad range of readers is the conscious decision to minimise both mathematical and programming notation and focus more on the core and practical concepts of Artificial Intelligence and its deployment. Once the reader has a good grasp of the concepts, understanding the theoretical principles becomes much easier.
The first chapter of this book will introduce the field of Artificial Intelligence, Machine Learning and Deep Learning. In the first section following the introduction (Chaps. ) will discuss the deployment and hosting of Machine Learning models within enterprise environments using frameworks such as TensorFlow Serving, Docker and Kubernetes.
There have been many contributing factors associated with the completion of this book, the most important being all the people who have helped me. I would first like to thank Dr. Carl Chalmers for his unwavering passion and commitment during the writing of this book. He has had to endure many grumpy moments with me over the last year or so. I would like to thank the Springer team for their help and support and for the excellent guidance they have given us during this project. I would especially like to thank my wife Lorna Bracegirdle for putting up with me over the years and for the support she has given me. I would also like to thank my son Benjamin Fergus, my step-daughter Sasha-Lei Bracegirdle, my mother-in-law Lillian Bracegirdle and my father-in-law Brian Bracegirdle, who sadly passed away before the book was completed, for standing by me and supporting me when I needed it. Lastly, I would like to thank my dog Milo who loves me unconditionally and always makes me laugh when I need it.
I would like to thank my wife Rachel and my two sons Joshua and Toby for their patience and support in writing this book. Without their support and encouragement, it would have been impossible to complete. I would also like to thank my friend and colleague Professor Paul Fergus for his support, wisdom and friendship throughout the entire processes. I would also like to thank the inspirational researchers mentioned throughout this book for their tireless dedication to the field of Artificial Intelligence.
We are now beginning to see the widespread use of Artificial Intelligence (AI) in all walks of life. From Alexa in the home to the promise of driverless cars in the future. Many aspects of AI have transitioned from a purely theoretical field to an applied one. Therefore, unlike traditional university courses, this book provides an introductory guide to those wishing to pick up AI and apply it in solving real-world problems. Never before have we seen so many frameworks surrounding the application of AI. With many large organisations such as Google, Microsoft, IBM, Facebook and NVidia offering a wide range of AI technologies, the race is on to capture market share as we continue to see the uptake of AI. This means that businesses both small and large are increasingly looking to use these technologies to start developing solutions to solve their own unique problems. This book is timely as its underlying goal is to bridge the gap between the well-supported frameworks that organisations provide and anyone with a desire to learn applied AI.
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