Covers: theory of Matrix Algebra
Estimated time needed to finish: 19 minutes
Questions this item addresses:
  • What are the most basic matrix manipulation techniques I need to know?
  • How easy does PyTorch make it to perform these operations?
How to use this item?

Learn abou from the vide lecture:

  • Dot Product and Matrix Multiplication
  • Matrix Transpose
  • Special Matrices: Diagonal, Identity, Null

Why to learn these operations first? In fact, PyTorch is not only a library enabeling you to perform "fit a model". Before traning a model, let's make a step back !

On granular level, deep learning is s rigorious application of linear algebra. Remember, even your training data will need to be respresented as a tensor.

Before training a model, you will need to transform your data into a format and shape your neural network can digest. Look at this official PyTorch documentation and l**earn how easy PyTorch makes it for you to perform these operations.

Look what you will learn about matrices in a nutshell!

  • Dot Product and Matrix Multiplication

  • Matrix Transpose Look here to have immediate idea about what this opperation looks like. In fact, matrix transposition (after matrix multiplication) is the most essential operation for making a NN architecture work. The main reason is that it makes it possible to multiply two matrices like: and . This opperation happens immediatelly when input training data (represented as a tensor) flows to fully connected layer.

Author(s) / creator(s) / reference(s)
Amir Hajian
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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

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