LeNet-5

LeNet-5 is a classical neural network architecture that was successfully used on a handwritten digit recognition problem back in 1998. In principle, LeNet-5 was the first architecture that introduced the idea of applying several convolution layers before connecting to a fully-connected hidden layer. Before that, people would construct the features manually and then connect to a simple neural network with many hidden layers and neurons.

Here's the LeNet 5 architecture:

According to the paper, http://users.cecs.anu.edu.au/~Tom.Gedeon/conf/ABCs2018/paper/ABCs2018_paper_57.pdf, this model was able to achieve 99.05% accuracy, which is quite ...

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