Easy clarification of convolutional neural community | Deep Learning Tutorial 23 (Tensorflow & Python)

A extremely very simple rationalization of convolutional neural community or CNN or ConvNet this sort of that even a higher faculty scholar can recognize it conveniently. This movie involves incredibly much less math and is excellent for total novice who doesn’t have any notion on what CNN is and how it will work. We will address unique subject areas this sort of as,

1. Why typically people are superior at picture recognition than pcs?
2. Shortcomings of making use of traditional synthetic neural network (ANN) for image classification.
3. How human brain recognizes illustrations or photos?
4. How computers can use filters for feature detection
5. What is convolution operation and how it performs
6. Worth of ReLU activation in CNN
7. Relevance of pooling procedure in CNN
8. How to handle rotation and scale in CNN

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Below are some good articles on CNN,

Is CNN scale/rotation invariant?
https://stats.stackexchange.com/queries/239076/about-cnn-kernels-and-scale-rotation-invariance#:~:text=22-,1)%20The%20features%20extracted%20employing%20CNN%20are%20scale%20and%20rotation,information%2C%20see%3A%20Deep%20Discovering.&text=Convolution%20is%20not%20obviously%20equivariant,or%20rotation%20of%20an%20impression.

https://ujjwalkarn.me/2016/08/11/intuitive-rationalization-convnets/
PDF Paper on cnn: http://www.deeplearningbook.org/contents/convnets.html

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