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### Foundations Of Algebra For Deep Learning

**Total time needed: **~2 hours- Objectives
- You will learn fundamental PyTorch operations with tensors for future DL/NN applications.
- Potential Use Cases
- Building NN from scratch
- Who is This For ?
- INTERMEDIATE

Click on each of the following **annotated items** to see details.

Resources4/6

VIDEO 1. Tensors, Matrices, Dot Product

- What are the most basic matrix manipulation techniques I need to know?
- How easy does PyTorch make it to perform these operations?

19 minutes

VIDEO 2. Matrices and Eigen-decomposition

- What is matrix determinant?
- What is matrix Eigendecomposition and what it is similar to (hint: PCA and SVD)?
- What are some special properties of positive-definite matrices?
- Do I need to know and understand all these operations to be a DL practitioner?

23 minutes

VIDEO 3. Mathematical Non-linearities

- How to solve eigendecomposition on a whiteboard?
- What is the relevance of nonlinearities for deep learning?

23 minutes

REPO 4. Hands-on Linear Algebra for Deep Learning

- How to carry out linear algebraic tasks for deep learning in PyTorch?

30 minutes

BOOK_CHAPTER 6. Linear Algebra for Deep Learning

- How is linear algebra used in deep learning?

20 minutes

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