Probabilistic deep learning with Python, Keras, and TensorFlow Probability
"A hands-on guide to the principles that support neural networks"--
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, New York :
Manning
[2020]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631459806719 |
Tabla de Contenidos:
- 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.
