Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability

Duerr, Oliver, Sick, Beate, Murina, Elvis



Probabilistic Deep Learning 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.


《機率深度學習》展示了機率深度學習模型如何為讀者提供工具,以識別和考慮結果中的不確定性和潛在錯誤。從將最大似然曲線擬合原理應用於深度學習開始,讀者將進一步使用基於Python的Tensorflow Probability框架,並建立能夠陳述其不確定性的貝葉斯神經網絡。購買印刷版書籍可獲得Manning Publications提供的PDF、Kindle和ePub格式的免費電子書。


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.


Oliver Duerr是德國康斯坦茨應用科學大學的數據科學教授。Beate Sick在ZHAW擔任應用統計學的教授,同時也是蘇黎世大學和ETH蘇黎世的研究員和講師。Elvis Murina是一位研究助理,負責本書的廣泛練習。Duerr和Sick都是機器學習和統計學的專家。他們指導了許多關於深度學習的學士、碩士和博士論文,並計劃和開設了多個深度學習的研究生和碩士課程。三位作者自2013年以來一直在使用深度學習方法,並在教授該主題和開發概率深度學習模型方面擁有豐富的經驗。