Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability (MEAP) by Oliver Dürr (Durr)
Requirements: .ePUB, .PDF, .MOBI/.AZW reader, 40 MB
Overview: 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.
Genre: Non-Fiction > Tech & Devices
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