2. Crashcourse in the Preliminaries¶
If you are in a hurry, this chapter has all the details you need to get a taste of deep learning. If you like to take the scenic route, we still recommend that you read it, since it will explain how to set up Apache MXNet, how to use automatic differentiation and how to manipulate data and memory. We also provide quick primers on linear algebra and statistics, designed to help readers to get up to speed on the basics.
- 2.1. Data Manipulation
- 2.2. Linear Algebra
- 2.2.1. Scalars
- 2.2.2. Vectors
- 2.2.3. Length, dimensionality and shape
- 2.2.4. Matrices
- 2.2.5. Tensors
- 2.2.6. Basic properties of tensor arithmetic
- 2.2.7. Sums and means
- 2.2.8. Dot products
- 2.2.9. Matrix-vector products
- 2.2.10. Matrix-matrix multiplication
- 2.2.11. Norms
- 2.2.12. Norms and objectives
- 2.2.13. Intermediate linear algebra
- 2.2.14. Summary
- 2.2.15. Scan the QR Code to Discuss
- 2.3. Automatic Differentiation
- 2.4. Probability and Statistics
- 2.5. Naive Bayes Classification
- 2.6. Sampling
- 2.7. Documentation