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Pyro: Sampling from the posterior
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Classroom Contents
Deep Probabilistic Modelling with Pyro
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- 1 Intro
- 2 Time Series Prediction
- 3 Multi-Sensor Systems
- 4 Deep Neural Networks - Limitations
- 5 Adversarial Attacks
- 6 Neural Networks Predictions
- 7 Neural Networks Bias
- 8 Conditional Probability
- 9 Inference from Data
- 10 Probabilistic Regression
- 11 Bayes Networks
- 12 Gaussian Processes
- 13 Probabilistic Neural Networks
- 14 Probabilistic Programming Languages
- 15 Pyro - Framework
- 16 Pyro/Py Torch Example: MNIST
- 17 Neural Network Softmax Prediction
- 18 Pyro: Weight Priors
- 19 Pyro: Inference
- 20 Pyro: Variational Inference
- 21 Pyro: Loss & Training
- 22 Pyro: Sampling from the posterior
- 23 Random Noise
- 24 Predictive Maintenance Example
- 25 Sensor Data 1
- 26 Neural Network Prediction
- 27 Summary
