Implementing Deep Learning Model for Object Detection

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Giving the ability to a computer or robot to know a shape or pattern in an image is a very difficult problem to solve but it is not impossible. The use of learning algorithms allows a computer to identify a shape in an image for classification, location or analysis. This shape can be a letter, a number or a real object. Many research works are presented in the field of pattern recognition to improve the performance of learning algorithms in terms of accuracy and speed. Since the 1940s researchers in artificial intelligence have been trying to unravel the secrets of the functioning of the human brain in the hope of being able to someday virtually replicate it and transfer it to machines that would become as intelligent and autonomous as human. Research progressed dramatically and began to design smart machines. AI used to be a fanciful concept from science fiction, but now it's becoming a daily reality. Neural networks are paving the way toward breakthroughs in machine learning, called "deep learning." Deep learning and computer vision are transforming the way we interact with all the machines and devices in our lives, by enabling them to process what they're seeing in real time.

Autorentext

Riadh Ayachi ha conseguito la laurea triennale in Elettronica, Elettrotecnica e Automatica nel 2015 e la laurea magistrale in Micro e Nano Elettronica nel 2018 presso la Facoltà di Scienze di Monastir, Tunisia, nel 2015. Attualmente sta preparando il suo dottorato di ricerca nell'area del deep learning, dell'elaborazione di immagini e video e dell'implementazione hardware.

Weitere Informationen

  • Allgemeine Informationen
    • Sprache Englisch
    • Anzahl Seiten 96
    • Herausgeber LAP LAMBERT Academic Publishing
    • Gewicht 161g
    • Untertitel Artificial Intelligence for Computer Vision
    • Autor Riadh Ayachi
    • Titel Implementing Deep Learning Model for Object Detection
    • Veröffentlichung 25.07.2019
    • ISBN 6200256845
    • Format Kartonierter Einband
    • EAN 9786200256843
    • Jahr 2019
    • Größe H220mm x B150mm x T6mm
    • GTIN 09786200256843

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