# Classic Networks

## LeNet-5

* It was developed by Yann LeCun et al. in 1998
* The goal of this network was to classify handwritten digits
* It was trained using 32x32x1 grayscale images![](https://3668624528-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RGo4dZhdwCIHrC1%2F-M5-0TEuN77zAmTpQjIr%2F-M5-0VF8lA7F-wL0A4jB%2FLeNet-5.JPG?generation=1586990830878430\&alt=media)
* This net is smaller compared to today's standards; it had about 60K parameters
* It is interesting to note that as the height and width of the image decreased across the layers, the number of channels increased
* Also, the net had a sigmoid non-linearity after the pooling layers, which is no longer used today after pooling

## AlexNet

![](https://3668624528-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RGo4dZhdwCIHrC1%2F-M5-0TEuN77zAmTpQjIr%2F-M5-0VFAYbqP0XbBlIIy%2FAlexNet.JPG?generation=1586990823989050\&alt=media)

* AlexNet was developed by Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever in 2012
* It is much bigger than LeNet-5 and has about 60M parameters

## VGG-16

* It was developed by K. Simonyan and A. Zisserman in 2014
* Instead of having thousands and thousands of parameters, this model used fixed parameters
  * All its convolutional layers had 3x3 filters with stride 1 and "same" padding
  * All its max-pooling layers were 2x2 and had stride 2
*   It had a very simplified architecture:

    ![](https://3668624528-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RGo4dZhdwCIHrC1%2F-M5-0TEuN77zAmTpQjIr%2F-M5-0VFCfVh384rHLyOh%2FVGG-16.png?generation=1586990823929629\&alt=media)
