Contributors

Covers: theory of Linear Algebra

Fail to play? Open the link directly: https://www.youtube.com/watch?v=Csa5R12jYRg

DeepLizard

0 comment

Coming soon

public

Contributors

- 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.

Resources

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

RECIPE 4. Matrix Algebra

10 minutes

REPO 5. Hands-on Linear Algebra for Deep Learning

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

30 minutes

VIDEO 6. Tensors and Data Structures

10 minutes

BOOK_CHAPTER 7. Linear Algebra for Deep Learning

- How is linear algebra used in deep learning?

20 minutes

0 comment