Chapter 29

Chapter 29 sections from Deep Learning with PyTorch.

7 items

Sections

  1. 29.1 Bayesian Neural Networks
  2. 29.2 Variational Inference
  3. 29.3 Monte Carlo Methods
  4. 29.4 Uncertainty Estimation
  5. 29.5 Gaussian Processes
  6. 29.6 Practical Probabilistic Modeling in PyTorch
  7. 29.7 Summary and Further Reading
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Bayesian Neural Networks

A Bayesian neural network is a neural network whose parameters are treated as random variables rather than fixed unknown constants.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Variational Inference

Bayesian neural networks require inference over a posterior distribution:
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Monte Carlo Methods

Monte Carlo methods approximate difficult mathematical quantities using random samples.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Uncertainty Estimation

Uncertainty estimation measures how much confidence a model should place in its own predictions.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Gaussian Processes

A Gaussian process is a probabilistic model over functions. Instead of defining a probability distribution over parameters, as in Bayesian neural networks, a Gaussian process defines a probability distribution directly
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Practical Probabilistic Modeling in PyTorch

Probabilistic deep learning adds distributions to ordinary neural networks.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part IX › Chapter 29 ›

Summary and Further Reading

Probabilistic deep learning extends neural networks with explicit probability models.
deep-learning pytorch