In this video I explain the first building blocks you need to understand Linear Algebra in the context of Machine Learning or Deep Learning.

We cover what scalars, matrices, vectors and tensors are, why you even need Linear Algebra, and then start with the first concepts related to matrices: transposing a matrix, scalar multiplication and broadcasting - which is more of a thing in Python and other programming languages than in math itself because it's a shortened notation.

This content is based on the Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville, which you can read for free at https://www.deeplearningbook.org/, where it was published by the authors.

In this video I cover the content of chapter 2.1 plus my own understanding and background on the topics :))


TIMESTAMPS:
0:00 Why Linear Algebra?
1:23 Scalars
1:50 Vectors
3:00 Matrices
3:39 Tensors
5:25 Transpose
6:16 Addition
6:45 Scalar Multiplication
7:06 Broadcasting


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Here is the content of this video in written form, if you quickly want to look something up or save some of the images for your private notes: https://galaxyinferno.com/linear-alge...