# 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![](/files/-M5-0VF8lA7F-wL0A4jB)
* 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

![](/files/-M5-0VFAYbqP0XbBlIIy)

* 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:

  ![](/files/-M5-0VFCfVh384rHLyOh)


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