Deep Learning and Practice with MindSpore. ISBN-13: 9789811622328, 978-9811622328.
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ISBN-13: 9783031296444, 978-3031296444. This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications.
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ISBN-13: 9781119845010, 978-1119845010. Format: Hardback.
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ISBN-13: 9781491914250, 978-1491914250. Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization?.
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How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
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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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Title: Introduction to Deep Learning Using R: A Step-by-Step Guide to L Item Condition: used item in a very good condition. Used-like N : The book pretty much look like a new book. There will be no stains or markings on the book, the cover is clean and crisp, the book will look unread, the only marks there may be are slight bumping marks to the edges of the book where it may have been on a shelf previously.
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This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems.
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