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Herausgeber: 
  • Gerard Prudhomme
  • Deep Learning Technologies and Applications 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  November 2017  
    Genre:  EDV / Informatik 
     
    Computers - General Information / COMPUTERS / General
    ISBN:  9781773612744 
    EAN-Code: 
    9781773612744 
    Verlag:  Arcler Press 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 229 mm / B 152 mm / D  
    Seiten:  336 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Deep learning tools could very well end up with a substantially higher standard of accuracy and reliability in the recognition of physical objects, in some instances more advanced than individual overall human performance. Deep learning is an outlet of understanding, or perhaps studying, that takes advantage of a number of levels of non-linear processor jobs to discover how you can make representations of highly effective daily processes unswervingly from computer data.The first chapter refers to deep learning. Chapter 2 shows that when provided with genomic variance computer data from a variety of people, calculating the chance of complicated populace hereditary designs can often be improbable. Chapter 3 looks at how live-cell imaging provides you with started out a thrilling range into the function cellular heterogeneity performs in vibrant, subsistence devices.Chapter 4 looks at how protein contacts provide you with crucial information and facts for the comprehension of protein frameworks. Chapter 5 suggests a structure for foretelling updates in electronic community end user behavior. Chapter 6 looks at how precise computational recognition of promoters continues to be an issue.Chapter 7 shows that an innovative intrusion detection system ( IDS ) making use of a deep neural network ( DNN ) is offered to improve the safety of in-vehicular system. Chapter 8 looks at how event identification is easily the most basic and also crucial job in event-based all-natural vocabulary processing devices. Chapter 9 looks at getting a grasp on the cell-specific merging designs of transcription factors.Chapter 10 looks at what exactly is the source of our capability to understand orthographic information. Chapter 11 displays comprehending the simplest way to understand blockage at one area.

      



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