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Covers: theory of Linear Algebra
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Author(s) / creator(s) / reference(s)
DeepLizard
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Foundations Of Algebra For Deep Learning

Contributors
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
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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

Concepts Covered

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