Название: Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability (MEAP Version 6) +codeАвтор: Oliver Durr, Beate Sick
Издательство: Manning Publications
Год: 2020
Формат: true pdf/epub
Страниц: 300
Размер: 67.2 Mb
Язык: English
About the Technology
Probabilistic deep learning models are better suited to dealing with the noise and uncertainty of real world data — a crucial factor for self-driving cars, scientific results, financial industries, and other accuracy-critical applications. By utilizing probabilistic techniques, deep learning engineers can judge how reliable their results are, and get a better understanding of how their algorithms function.
About the book
Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability shows how probabilistic deep learning models gives you the tools to identify and account for uncertainty and potential errors in your results. Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, you’ll move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties. Hands-on code examples and illustrative Jupyter notebooks ensure that you’re focused on the practical applications of the abstract-but-powerful concepts of probabilistic deep learning. By the time you’re done, you’ll be able to build highly-performant applications that can account for inaccuracies without constantly running, and re-running, your models.