Hands-On Transfer Learning with Python

LeNet architecture

This is a pioneering seven-level convolutional network, designed by LeCun and their co-authors in 1998, that was used for digit classification. Later, it was applied by several banks to recognize handwritten numbers on cheques. The lower layers of the network are composed of alternating convolution and max pooling layers.

The upper layers are fully connected, dense MLPs (formed of hidden layers and logistic regression). The input to the first fully connected layer is the set of all the feature maps of the previous layer:

After the successful application of CNNs for digit classification, researchers focused on building more ...