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Autor(en): 
  • Ugail Hassan
  • Deep Learning in Visual Computing: Explanations and Examples 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juli 2022  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Artificial Intelligence / Automatic control engineering / CNN Feature / CNN Model / COMPUTERS / Computer Engineering / COMPUTERS / Human-Computer Interaction (HCI) / COMPUTERS / Machine Theory / Convolution Layers
    ISBN:  9780367549626 
    EAN-Code: 
    9780367549626 
    Verlag:  Taylor and Francis 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 216 mm / B 138 mm / D  
    Gewicht:  453 gr 
    Seiten:  140 
    Illustration:  schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing. This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.

      



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