# Non-linear hypothesis

- Outdated but most powerful learning algorithm in most ML problems

- the number of features is increasing as big theta notation
- even only quadratic feature get picked, only get circle, not the more complex one
- if not the the quadratic get picked, the order will increase a lot more, close to 170.000 features..
- for many, examples, n tend to increase very large

- examples of car image
- thr computer vision is hard, that's way
- if we want to tell computer to recognize the image
- the computer analyze the door knob

- need to differentiate the cars and non cars
- high computational expensive
- n = 2500, for grayscale, 7500 for RGB, 3 million if quadratic included
- to put it simply, the logistic regression is expensive for lots of features
- here's the neural networks actually works, solving a non-linear problems with a lot of complex features

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