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Foundations Of Algebra For Deep Learning
Total time needed:
You will learn fundamental PyTorch operations with tensors for future DL/NN applications.
Potential Use Cases
Building NN from scratch
Who is This For ?
Click on each of the following
to see details.
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?
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?
3. Mathematical Non-linearities
How to solve eigendecomposition on a whiteboard?
What is the relevance of nonlinearities for deep learning?
4. Hands-on Linear Algebra for Deep Learning
How to carry out linear algebraic tasks for deep learning in PyTorch?
5. Matrix Algebra
6. Linear Algebra for Deep Learning
How is linear algebra used in deep learning?