Bayesian Inference for Robust Deep Learning

Bayesian modeling assesses event probabilities given prior assumptions and observations. Initially, we consider all possible outcomes when rolling two six-sided dice. There are 36 total outcomes, with 4 combinations summing to 5. Thus, the initial probability (prior) of rolling a sum of 5 is 4/36. If we know one die's value (e.g., 3), our possible values shrink to 6, requiring the other die to be 2 for a sum of 5. With a fair die, the new probability (posterior) of rolling a sum of 5 is 1/6. Bayesian statistics uses Bayes' rule to calculate posterior probabilities. In this book, we'll often discuss uncertainties in deep learning models, predicting P(|) where is a model's prediction and are its parameters. Uncertainties help develop more robust deep learning systems.

• Revisits Bayesian modeling
• Elucidates Bayesian inference through sampling
• Introduces Gaussian processes
• Lays groundwork for understanding Bayesian inference in deep learning
• Integrates uncertainty quantification into deep learning models