Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparison of Optimization Algorithms for Deep Learning   In recent years, we have witnessed the rise of deep learning. Deep neural
networks have proved their success in many areas. However, the optimization of
these networks has become more difficult as neural networks going deeper and
datasets becoming bigger. Therefore, more advanced optimization algorithms have
been proposed over the past years. In this study, widely used optimization
algorithms for deep learning are examined in detail. To this end, these
algorithms called adaptive gradient methods are implemented for both supervised
and unsupervised tasks. The behaviour of the algorithms during training and
results on four image datasets, namely, MNIST, CIFAR-10, Kaggle Flowers and
Labeled Faces in the Wild are compared by pointing out their differences
against basic optimization algorithms.

9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th