Articoli correlati a Introduction to Deep Learning
Sinossi
This book is designed to provide a comprehensive introduction to the field of deep learning, covering its foundational principles, techniques, and applications. It covers topics such as neural networks, convolutional networks, recurrent networks, and deep reinforcement learning. The content emphasizes both the theoretical concepts and practical implementations of deep learning models, providing insights into how these models are trained and applied to solve complex problems. Practical examples and hands-on exercises are included to help readers develop a solid understanding of deep learning techniques and their applications in various fields.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull'autore
Mauricio Alberto Ortega-Ruíz is an Electrical Engineering graduate from UNAM at Mexico, with experience in technical support for electronics equipment, field service and training services. This experience accomplishes instrumentation equipment, photo-lab and automation industry. Mauricio's academic journey includes a M. Sc. in signal processing at the Imperial College of Science Technology and Medicine and a PhD at City University of London, both are UK Universities. His main Research interest is in AI applications for medical imaging analysis and particularly in digital histopathology for breast cancer grading, he has published Scientific papers on this topic and participated in the Automated Gleason Grand Challenge 2022 in which he developed Deep Learning methods for Prostate cancer image grading and obtained the 10th place in the final ranking. He is cofounder of DigPatho, a research group for the LATAM region. Besides his passion for Research in the medical image field he has also demonstrated interest in signal processing, and other imaging applications. He dedicates time to culture and music, and as an amateur violist, he was member of the Imperial College Chamber orchestra during his masters.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Paperback. Condizione: new. Paperback. This book is designed to provide a comprehensive introduction to the field of deep learning, covering its foundational principles, techniques, and applications. It covers topics such as neural networks, convolutional networks, recurrent networks, and deep reinforcement learning. The content emphasizes both the theoretical concepts and practical implementations of deep learning models, providing insights into how these models are trained and applied to solve complex problems. Practical examples and hands-on exercises are included to help readers develop a solid understanding of deep learning techniques and their applications in various fields. Delve into deep learning fundamentals where theory meets practice. Explore neural networks, convolutional and recurrent architectures, and deep reinforcement learning. Hands-on exercises and examples illuminate model training and innovative problem-solving across diverse fields. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781779562999
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