Probabilistic deep learning : with Python, Keras, and TensorFlow Probability /

Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy...

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Detalles Bibliográficos
Autores principales: Dürr, Oliver (College teacher) (Autor), Sick, Beate (Autor), Murina, Elvis (Autor)
Formato: eBook
Idioma:Inglés
Publicado: Shelter Island, New York : Manning Publications, [2020]
Temas:
Machine learning.
Neural networks (Computer science)
Apprentissage automatique.
Réseaux neuronaux (Informatique)
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.
Notas:"Exercises in Jupyter Notebooks"--Cover
Descripción Física:1 online resource
ISBN:9781638350408
163835040X
1617296074
9781617296079

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