A minimal convolutional neural network for handwritten digit recognition
Matthew Y. W. Teow · 2017
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network using a minimal model. The proposed minimal convolutional neural network is presented using a layering approach. This approach provides a clear understanding of the main mathematical operations in a convolutional neural network. Hence, it benefits beginners and non-mathematical prolific researchers to understand the operation of a convolutional neural network without having an intimidating experience. A handwritten digit recognition using MNIST handwritten digit dataset is used to experiment the performance of the proposed minimal convolutional neural network.