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Probabilistic Deep Learning in TensorFlow - The Why and How - ODSC Europe 2019
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- 1 Intro
- 2 Overview
- 3 TensorFlow dependencies
- 4 Building a regression model
- 5 Exploring the data
- 6 Creating a chaos model
- 7 Defining a helper class
- 8 Results
- 9 Early stopping
- 10 Plotting predictions
- 11 Takeaways
- 12 Replacing Dense Layer
- 13 Dense Variational
- 14 Prior Distribution
- 15 Example Batch
- 16 The Trick
- 17 Inspecting Layers
- 18 Model Results
- 19 Model Predictions
- 20 Input Predictions
- 21 Call Banks
- 22 Plot History
- 23 Summary
- 24 Regression with probabilistic layers
- 25 Negative log likelihood