Covers: theory of Linear Algebra
Estimated time needed to finish: 20 minutes
Questions this item addresses:
  • How is linear algebra used in deep learning?
How to use this item?

This is one of the first chapters of the fameous "deep learning" book. Skim through the chapter and pause on any topic that you're less familiar with

Author(s) / creator(s) / reference(s)
Ian Goodfellow, et al
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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
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
RECIPE 5. Matrix Algebra
10 minutes
BOOK_CHAPTER 6. Linear Algebra for Deep Learning
  • How is linear algebra used in deep learning?
20 minutes

Concepts Covered

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