> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Calculus for Machine Learning

> Calculus fundamentals including derivatives and matrix calculus.

<Tip>
  [Table of Simple Derivatives](https://en.wikibooks.org/wiki/Calculus/Tables_of_Derivatives)
</Tip>

For this course you need to review some basic calculus. The best resource for it is [Khan Academy's Multivariate Calculus](https://www.khanacademy.org/math/multivariable-calculus). Go through the videos and exercises in Units 1 and 2 only.

For more advanced reader, and specifically for fully understanding deep learning that includes *matrix calculus*  there is a very good reference [here](https://arxiv.org/pdf/1802.01528.pdf).

We have also shot a recitation video for you to review some of the concepts.

<iframe width="560" height="315" src="https://www.youtube.com/embed/Ygw9jR42y9I" frameBorder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowFullScreen />

*Calculus recitation for my classes. Recitation was delivered by my TA Pritam Sen.*

## References

T. Parr and J. Howard, “The Matrix Calculus You Need For Deep Learning,” arXiv \[cs.LG], Feb. 05, 2018. Available: [http://arxiv.org/abs/1802.01528](http://arxiv.org/abs/1802.01528)

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