Probabilistic Deep Learning with Python

Probabilistic Deep Learning with Python
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Book Description
Probabilistic Deep Learning with Python shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results.
Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties.

Content

PART 1 BASICS OF DEEP LEARNING
1. Introduction to probabilistic deep learning
2. Neural network architectures
3. Principles of curve fitting

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

PART 3 BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS
7. Bayesian learning
8. Bayesian neural networks

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