Implementation of Training Convolutional Neural Networks
This work provides an incremental implementation of CNNs for face recognition, with a focus on parallel computing efficiency.
The authors implemented a convolutional neural network in Java for face recognition and proposed a parallelization strategy, achieving theoretical analysis of maximum speedup and parallel efficiency through timing measurements of forward and backward computations.
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied the particular convolutional neural network to implement the typical face recognition problem by java. Then, a parallel strategy was proposed in section4. In addition, by measuring the actual time of forward and backward computing, we analysed the maximal speed up and parallel efficiency theoretically.