Yann le Cun proposed (for more information refer to: Convolutional Networks for Images, Speech, and Time-Series, by Y. LeCun and Y. Bengio, brain theory neural networks, vol. 3361, 1995) a family of ConvNets named LeNet trained for recognizing MNIST handwritten characters with robustness to simple geometric transformations and to distortion. The key intuition here is to have low-layers alternating convolution operations with max-pooling operations. The convolution operations are based on carefully chosen local receptive fields with shared weights for multiple feature maps. Then, higher levels are fully connected layers based on a traditional MLP with hidden layers and softmax as the output layer.
An example of DCNN — LeNet
LeNet code in Keras
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