A Survey of Deep-learning Frameworks

被引：0

作者：

Parvat, Aniruddha ^{[1
]}

Chavan, Jai ^{[1
]}

Kadam, Siddhesh ^{[1
]}

Dev, Souradeep ^{[1
]}

Pathak, Vidhi ^{[1
]}

机构：

[1] Sinhgad Inst Technol, Dept Comp Engn, Lonavala 410401, Maharashtra, India

来源：

PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON INVENTIVE SYSTEMS AND CONTROL (ICISC 2017) | 2017年

关键词：

Machine learning; Deep learning; Neural networks; Software libraries;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Deep learning is a model of machine learning loosely based on our brain. Artificial neural network has been around since the 1950s, but recent advances in hardware like graphical processing units (GPU), software like cuDNN, TensorFlow, Torch, Caffe, Theano, Deeplearning4j, etc. and new training methods have made training artificial neural networks fast and easy. In this paper, we are comparing some of the deep learning frameworks on the basis of parameters like modeling capability, interfaces available, platforms supported, parallelizing techniques supported, availability of pre-trained models, community support and documentation quality.

引用

页码：211 / 217

页数：7

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