Optimizing Convolutional Neural Network Parameters for Better Image Classification

The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.