# Simple numerical differentiation

**URL:** <https://discourse.julialang.org/t/simple-numerical-differentiation/52254>\
**Category:** New to Julia\
**Tags:** question, forwarddiff, finitediff\
**Created:** [December 23, 2020, 2:55am UTC](https://discourse.julialang.org/t/simple-numerical-differentiation/52254 "2020-12-23T02:55:31Z")\
**Posts on this page:** 1\
**Showing post:** 5

<div class="post-metadata">

**Author:** ![zdenek\_hurak](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/zdenek_hurak/32/53118_2.png) [@zdenek\_hurak](https://discourse.julialang.org/u/zdenek_hurak)\
**Post date:** [December 23, 2020, 10:21am UTC](https://discourse.julialang.org/t/simple-numerical-differentiation/52254/5 "2020-12-23T10:21:11Z")

</div>

Note that what `ForwardDiff` package offers is not numerical differentiation. There is now a trio of approaches to differentiation:

- symbolic: `Sympy`, …
- numerical: `FiniteDifferences`, `FiniteDiff`, …
- [algorithmic](https://en.wikipedia.org/wiki/Automatic_differentiation): `ForwardDiff`, `Yota`, `Zygote`, `ReverseDiff`,…

---

_[View the full topic](https://discourse.julialang.org/t/simple-numerical-differentiation/52254)._