#48: Scikit-learn 45:Supervised Learning 23: Image augmentation in Python

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The video discusses the intuition for image data augmentation followed by coding in Python.

Timeline
(Python 3.8)

[Note: There is a lag in video and audio.]

00:00 - Outline of video
00:29 - What is image data augmentation?
01:39 - Why do data augmentation?
01:51 - Example of image augmentation
02:42 - Open Jupyter notebook
03:15 - Create a custom image augmentation function: read image
07:54 - Create a custom image augmentation function: rotate image
10:36 - Create a custom image augmentation function: shear
11:42 - * * * Note: Video lags audio * * *
13:33 - Create a custom image augmentation function: wrap
15:00 - Create a custom image augmentation function: noise
15:52 - Create a custom image augmentation function: blur
16:40 - Create a custom image augmentation function: flip
17:17 - Create a custom image augmentation function: List of augmented images
18:10 - Create a custom image augmentation function: Lables for augmented images
18:40 - Plot augmented images
21:24 - Ending notes

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Code for image augment function:

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Комментарии

Hi Nilesh,
Image augmentation was very interesting!
But when you manipulate the same dataset does it not introduce multicollinearity into the model and lead to overfitting of data?

kshitijdesai

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