ISBN-13: 9783030716752, 978-3030716752. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. The chapters presented in this book were selected for quality and relevance.
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Introduction. The Instruction Paradigm. The Deep Learning Process and the Construction of Knowledge. Goals and Performance. Collaborative Learning. Academic Reading and Deep Learning. Deep Writing. Inclusive Deep Learning Environments. Time and Deep Learning: Intensive Teaching. Internationalization and Deep Learning. Evaluating to Learn. Student Evaluation of Teaching: Evaluating Deep Learning. The Learning Paradigm. Final Summary.
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ISBN-13: 9781119543046, 978-1119543046. Deep Learning for Dummies gives you the information you need to take the mystery out of the topicand all of the underlying technologies associated with it. Deep Learning For Dummies Author(s): John Paul Mueller, Luca Massaron.
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ISBN-13: 9781107661363, 978-1107661363. Deep Learning. Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances.
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Delve into the world of artificial intelligence with "Deep Learning with Python" by Francois Chollet, a comprehensive guide that brings the power of deep learning to your fingertips. Whether you're a student, researcher, or practitioner, this book offers an insightful journey through the complexities of neural networks and machine learning algorithms. The second edition of this acclaimed resource is presented in English and stands as a testament to Chollet's engaging writing style and expert instruction. It's an essential addition to any technical library, providing practical examples and code snippets that illustrate deep learning concepts and their real-world applications.
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ISBN-13: 9783031206382, 978-3031206382. This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures.
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