# Isn't anything but neural networks obsolete?

**URL:** <https://community.deeplearning.ai/t/isnt-anything-but-neural-networks-obsolete/246566>\
**Category:** Advanced Learning Algorithms\
**Tags:** week-module-3\
**Created:** [December 24, 2022, 9:20am UTC](https://community.deeplearning.ai/t/isnt-anything-but-neural-networks-obsolete/246566 "2022-12-24T09:20:00Z")\
**Posts on this page:** 1\
**Showing post:** 12

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**Author:** ![Christian\_Simonis](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/christian_simonis/32/142583_2.png) [@Christian\_Simonis](https://community.deeplearning.ai/u/Christian_Simonis)\
**Post date:** [December 25, 2022, 11:32am UTC](https://community.deeplearning.ai/t/isnt-anything-but-neural-networks-obsolete/246566/12 "2022-12-25T11:32:01Z")

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For guidance of the process along developing AI models the [CRISP-DM](https://www.datascience-pm.com/crisp-dm-2/) model is worth to mention, see also this [example](https://colab.research.google.com/drive/1hm62KGGqsPgkXsSk4rl-ZG4N9hlxrzpb) as some practical guidance. Feel free to take a closer look at it since it is some standard for data mining since several years even though it might not be perfect.

With respect to NN as possible “gold standard” in addition to the previous discussion above, I recommend to take a look at the _No-free-lunch-theorem_:

> In [statistics](https://en.m.wikipedia.org/wiki/Statistics), the term has been used to describe the tradeoffs of statistical learners (e.g., in [machine learning](https://en.m.wikipedia.org/wiki/Machine_learning)) which are unavoidable according to the [“No free lunch” theorem](https://en.m.wikipedia.org/wiki/No_free_lunch_theorem). That is, any model that claims to offer superior flexibility in analyzing data patterns usually does so at the cost of introducing extra assumptions, or by sacrificing generalizability in important situations.

[Source](https://en.m.wikipedia.org/wiki/There_ain%27t_no_such_thing_as_a_free_lunch)

Happy holidays!

Best  
Christian

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