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Taschenbuch. Zustand: Neu. Apprentissage Automatique Embarqué | Afef Mdhaffar | Taschenbuch | Französisch | 2024 | Éditions universitaires européennes | EAN 9786206706632 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Structures de Données en C: Ressources d'Apprentissage Intégrées | Support de Cours, Exercices Corrigés, Travaux Dirigés et Travaux Pratiques | Afef Mdhaffar | Taschenbuch | Französisch | 2024 | Éditions universitaires européennes | EAN 9786206706496 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model. All these techniques have been detailed in this book and illustrated with practical Python implementations, generally based on the use of the pytorch and tensorflow libraries. 56 pp. Englisch.…

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model. All these techniques have been detailed in this book and illustrated with practical Python implementations, generally based on the use of the pytorch and tensorflow libraries. …

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Französisch.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model. All these techniques have been detailed in this book and illustrated with practical Python implementations, generally based on the use of the pytorch and tensorflow libraries.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. …

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Este libro representa un primer paso hacia el aprendizaje automático integrado. Presenta técnicas para optimizar y comprimir modelos de aprendizaje profundo. Estas técnicas facilitan el despliegue de un modelo de aprendizaje profundo ligero y de alto rendimiento en dispositivos con recursos limitados, como smartphones y microcontroladores. Este artículo también explora una técnica de transferencia de conocimientos de actualidad, a saber, la destilación de conocimientos. Esta técnica permite mejorar el rendimiento de un modelo de aprendizaje profundo ligero, al tiempo que se le transfieren los conocimientos de un modelo de aprendizaje profundo complejo y de alto rendimiento. Todas estas técnicas se detallan en este libro y se ilustran mediante implementaciones prácticas en Python, basadas generalmente en el uso de las bibliotecas pytorch y tensorflow.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Este libro representa un primer paso hacia el aprendizaje automático integrado. Presenta técnicas para optimizar y comprimir modelos de aprendizaje profundo. Estas técnicas facilitan el despliegue de un modelo de aprendizaje profundo ligero y de alto rendimiento en dispositivos con recursos limitados, como smartphones y microcontroladores. Este artículo también explora una técnica de transferencia de conocimientos de actualidad, a saber, la destilación de conocimientos. Esta técnica permite mejorar el rendimiento de un modelo de aprendizaje profundo ligero, al tiempo que se le transfieren los conocimientos de un modelo de aprendizaje profundo complejo y de alto rendimiento. Todas estas técnicas se detallan en este libro y se ilustran mediante implementaciones prácticas en Python, basadas generalmente en el uso de las bibliotecas pytorch y tensorflow.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Spanisch.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 104 pp. Französisch.