Includes index. "Exercises in Jupyter Notebooks"--Page 1 of cover.
Contents:
Part 1, Basics of deep learning. Introduction to probabilistic deep learning ; Neural network architectures ; Principles of curve fitting -- Part 2, Maximum likelihood approaches for probabilistic DL models. Building loss functions with the likelihood approach ; Probabilistic deep learning models with TensorFlow Probability ; Probabilistic deep learning models in the wild -- Part 3, Bayesian approaches for probabilistic DL models. Bayesian learning ; Bayesian neural networks.
Summary:
"A hands-on guide to the principles that support neural networks"--Page 4 of cover.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.