Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability - Brossura

Durr, Oliver; Sick, Beate

Brossura

ISBN 10: 1617296074 ISBN 13: 9781617296079

Casa editrice: Manning Pubns Co, 2020

Riassunto

Probabilistic Deep Learning with Python shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results.

Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Su questo libro

Informazioni sugli autori

Oliver Duerr is professor for data science at the University of Applied Sciences in Konstanz, Germany.

Beate Sick holds a chair for applied statistics at ZHAW, and works as a researcher and lecturer at the University of Zurich, and as a lecturer at ETH Zurich.

Elvis Murina is a research assistant, responsible for the extensive exercises that accompany this book.

Oliver Duerr is professor for data science at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW, and works as a researcher and lecturer at the University of Zurich, and as a lecturer at ETH Zurich. Elvis Murina is a research assistant, responsible for the extensive exercises that accompany this book.

Duerr and Sick are both experts in machine learning and statistics. They have supervised numerous bachelors, masters, and PhD theses on the topic of deep learning, and planned and conducted several postgraduate and masters- level deep learning courses. All three authors have been working with deep learning methods since 2013 and have extensive experience in both teaching the topic and developing probabilistic deep learning models.

Dettagli dell?edizione

• Editore: Manning Pubns Co
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1617296074
• ISBN 13: 9781617296079
• Rilegatura: Copertina flessibile
• Numero di pagine: 274
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile