This book focuses on artificial intelligence and deep learning approaches, with case studies to solve problems associated with biometric security such as authentication, spoofing attack detection, and object detection. > This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc.
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ISBN-13: 9789811567612, 978-9811567612. This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). Format: Paperback.
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However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society.
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About the Author Jeff Heaton, PhD, is a computer scientist that specializes in data science and artificial intelligence. Specializing in Python, R, Java and C#, he is an open source contributor and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master's Degree in Information Management from Washington University and a PhD in computer science from Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project, a senior member of IEEE, and a fellow of the Life Management Institute (FLMI).
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From the Back Cover This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. Youll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general.
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ISBN-13: 9783030145224, 978-3030145224. Theory and Applications. Publisher: Springer Nature Switzerland AG, Switzerland.
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Publisher : MIT Press Illustrated edition (16 Aug 2019). Title: Deep Learning (MIT Press Essential Knowledge series). Order Processing & Cancellation.
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