Optimizing Convolutional Neural Network Parameters for Better Image
Classification
Abstract
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.