Deep Learning: Computational Aspects
Machine Learning
2019-08-30 v2 Computation
Machine Learning
Abstract
In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high-dimensional input-output models. Training a deep learning architecture is computationally intensive, and efficient linear algebra libraries is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive data sets.
Cite
@article{arxiv.1808.08618,
title = {Deep Learning: Computational Aspects},
author = {Nicholas Polson and Vadim Sokolov},
journal= {arXiv preprint arXiv:1808.08618},
year = {2019}
}