November 2020
Intermediate to advanced
296 pages
9h 8m
English
Part 1. Basics of deep learning
1 Introduction to probabilistic deep learning
2 Neural network architectures
Part 2. Maximum likelihood approaches for probabilistic DL models
4 Building loss functions with the likelihood approach
5 Probabilistic deep learning models with TensorFlow Probability
6 Probabilistic deep learning models in the wild