This tutorial explores Bayesian approaches to deep learning, focusing on uncertainty quantification and probabilistic modeling. You'll learn how to build neural networks that can express uncertainty in their predictions, implement various Bayesian neural network techniques, and understand when and why to use them.
- Introduction to Bayesian deep learning
- Monte Carlo Dropout for uncertainty estimation
- Bayesian Neural Networks with variational inference
- Deep ensembles
- Gaussian processes and neural networks
- Uncertainty calibration
- Applications and best practices
- Understand uncertainty in neural networks
- Implement Monte Carlo Dropout
- Build Bayesian neural networks with PyTorch
- Create and train deep ensembles
- Quantify and calibrate uncertainty
- Apply Bayesian methods to real problems
- Strong understanding of deep learning
- Basic probability and statistics
- Familiarity with Bayesian inference concepts
- PyTorch fundamentals
- Epistemic Uncertainty: Model uncertainty
- Aleatoric Uncertainty: Data uncertainty
- Variational Inference: Approximate Bayesian inference
- Posterior Distribution: Distribution over model parameters
- Predictive Uncertainty: Uncertainty in predictions
- Medical diagnosis with confidence estimates
- Autonomous driving safety
- Financial risk assessment
- Active learning
- Out-of-distribution detection
- Robust decision making
After this tutorial, you'll be able to build neural networks that know what they don't know, crucial for safety-critical applications and informed decision-making.