Deep Neural Network Design (48 results)

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Paperback or Softback. Condition: New. Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and Tensorflow Models Using Python. Book.

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Condition: New. 2022. 1st ed. Paperback. . . . . .

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Paperback. Condition: Brand New. 401 pages. 10.00x7.00x0.83 inches. In Stock.

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paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2024-09 Pages: 168 Publisher: Tsinghua University Press This book first introduces deep learning and compares it with other machine learning models. explaining techniques complementary to TensorFlow for creating deep learning models. such as Panda. Scikit-Learn. and NumPy. It then introduces supervised deep learning models. building shallow neural networks using multiple perceptrons in a single layer. and creating real-world applications using TensorFlow . …

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Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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Hardback. Condition: New. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction. Edited by an acknowledged expert, and with contributions from an international team of authors, this book provides a solid introduction to the fundamentals of radar and machine learning, and then goes on to explore a range of technologies, applications and challenges in this developing field. This book is also a valuable resource for both radar engineers seeking to learn more about deep learning, as well as computer scientists who are seeking to explore novel applications of machine learning. In an era where the applications of RF sensing are multiplying by the day, this book serves as an easily accessible primer on the nuances of deep learning for radar applications.…

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Taschenbuch. Condition: Neu. Automated Deep Learning Using Neural Network Intelligence | Develop and Design Pytorch and Tensorflow Models Using Python | Ivan Gridin | Taschenbuch | xvii | Englisch | 2022 | Apress | EAN 9781484281482 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Condition: Hervorragend. Zustand: Hervorragend | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | Intermediate-Advanced user level.

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Hardback. Condition: New. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction. Edited by an acknowledged expert, and with contributions from an international team of authors, this book provides a solid introduction to the fundamentals of radar and machine learning, and then goes on to explore a range of technologies, applications and challenges in this developing field. This book is also a valuable resource for both radar engineers seeking to learn more about deep learning, as well as computer scientists who are seeking to explore novel applications of machine learning. In an era where the applications of RF sensing are multiplying by the day, this book serves as an easily accessible primer on the nuances of deep learning for radar applications.…

Hardcover. Condition: Brand New. 400 pages. 9.21x6.14x0.94 inches. In Stock.