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Die Revolution der neuronalen Netze: Daten in Wissen verwandeln

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Description

Dieses Buch bietet eine umfassende Erforschung des Deep Learning, beginnend mit den Grundlagen neuronaler Netze, einschließlich des Perceptron-Algorithmus und Schlüsseltechniken wie Feed-Forward und Backpropagation, Optimierung und Regularisierung. Es befasst sich mit den Grundlagen des Deep Learning und behandelt wichtige Konzepte wie Gradientenabstieg, Backpropagation und Lösungen für Herausforderungen wie das Problem des verschwindenden Gradienten. Anschließend werden Faltungsneuronale Netze (CNNs) vorgestellt und ihre Architekturen, Faltungsschichten und Pooling-Schichten sowie Anwendungen wie Transfer Learning für die Bildklassifizierung erklärt. Darüber hinaus werden fortgeschrittene Deep-Learning-Architekturen wie LSTMs, GRUs und Autoencoder behandelt, einschließlich verschiedener Typen wie Sparse, Denoising und adversarial generative networks. Schließlich behandelt das Buch eine breite Palette von Anwendungen des Deep Learning, von der Bildverarbeitung und -segmentierung bis hin zur Objekterkennung, Video-zu-Text-Generierung und Dialogsystemen unter Verwendung von LSTMs, und bietet sowohl theoretisches Verständnis als auch praktische Einblicke für die Implementierung von Deep-Learning-Modellen.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
26 July 2025
Listed Since
27 July 2025

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