  Training of convolutional neural networks is a high dimensional and a
non-convex optimization problem. At present, it is inefficient in situations
where parametric learning rates can not be confidently set. Some past works
have introduced Newton methods for training deep neural networks. Newton
methods for convolutional neural networks involve complicated operations.
Finding the Hessian matrix in second-order methods becomes very complex as we
mainly use the finite differences method with the image data. Newton methods
for convolutional neural networks deals with this by using the sub-sampled
Hessian Newton methods. In this paper, we have used the complete data instead
of the sub-sampled methods that only handle partial data at a time. Further, we
have used parallel processing instead of serial processing in mini-batch
computations. The results obtained using parallel processing in this study,
outperform the time taken by the previous approach.
