Deep Learning With TF 2.0: 04.00- Numerical Computation
Jun 23, 2019
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
Deep Learning With TF 2.0: 02.00- Linear Algebra
May 19, 2019
- 02.00 - Linear Algebra
- 02.01 - Scalars, Vectors, Matrices and Tensors
- 02.02 - Multiplying Matrices and Vectors
- 02.03 - Identity and Inverse Matrices
- 02.04 - Linear Dependence and Span
- 02.05 - Norms
- 02.06 - Special Kinds of Matrices and Vectors
- 02.07 - Eigendecomposition
- 02.08 - Singular Value Decomposition
- 02.09 - The Moore-Penrose Pseudoinverse
- 02.10 - The Trace Operator
- 02.11 - The Determinant
- 02.12 - Example: Principal Components Analysis