2.1 Data Manipulation

2.1. Data Manipulation
2.1.1. Getting Started
2.1.2. Operations
2.1.3. Broadcasting Mechanism
2.1.4. Indexing and Slicing
2.1.5. Saving Memory
2.1.6. Conversion to Other Python Objects
2.1.7. Summary
2.1.8. Exercises
2.2. Data Preprocessing
2.2.1. Reading the Dataset
2.2.2. Handling Missing Data
2.2.3. Conversion to the Tensor Format
2.2.4. Summary
2.2.5. Exercises
2.3. Linear Algebra
2.3.1. Scalars
2.3.2. Vectors
2.3.3. Matrices
2.3.4. Tensors
2.3.5. Basic Properties of Tensor Arithmetic
2.3.6. Reduction
2.3.7. Dot Products
2.3.8. Matrix-Vector Products
2.3.9. Matrix-Matrix Multiplication
2.3.10. Norms
2.3.11. More on Linear Algebra
2.3.12. Summary
2.3.13. Exercises
2.4. Calculus
2.4.1. Derivatives and Differentiation
2.4.2. Partial Derivatives
2.4.3. Gradients
2.4.4. Chain Rule
2.4.5. Summary
2.4.6. Exercises
2.5. Automatic Differentiation
2.5.1. A Simple Example
2.5.2. Backward for Non-Scalar Variables
2.5.3. Detaching Computation
2.5.4. Computing the Gradient of Python Control Flow
2.5.5. Summary
2.5.6. Exercises
2.6. Probability
2.6.1. Basic Probability Theory
2.6.2. Dealing with Multiple Random Variables
2.6.3. Expectation and Variance
2.6.4. Summary
2.6.5. Exercises
2.7. Documentation
2.7.1. Finding All the Functions and Classes in a Module
2.7.2. Finding the Usage of Specific Functions and Classes
2.7.3. Summary
2.7.4. Exercises

[sergeyv]

Hello Alex,
thank you for a very good «intro» and neat explanation. Just to make sure, I hope you are going to provide further chapters in this series using examples in PyTorch.