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Více informací o knize Interpretability in Deep Learning

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Anotace knihy

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence

• Plný název: Interpretability in Deep Learning
• Autor: Ayush Somani, Alexander Horsch, Dilip K. Prasad
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 463
• EAN: 9783031206382
• ID: 41620668
• Nakladatelství: Springer, Berlin
• Hmotnost: 948 g
• Rozměry: 235 × 155 mm
• Rok vydání: 2023

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