Deep Learning With TF 2.0: 03.00- Probability and Information Theory
Jun 2, 2019
- 03.00 - Probability and Information Theory
- 03.01 - Why Probability?
- 03.02 - Random Variables
- 03.03 - Probability Distributions
- 03.04 - Marginal Probability
- 03.05 - Conditional Probability
- 03.06 - The Chain Rule of Conditional Probabilities
- 03.07 - Independence and Conditional Independence
- 03.08 - Expectation, Variance and Covariance
- 03.09 - Common Probability Distributions
- 03.10 - Useful Properties of Common Functions
- 03.11 - Bayes’ Rule
- 03.12 - Technical Details of Continuous Variables
- 03.13 - Information Theory
- 03.14 - Structured Probabilistic Models