China
Africa
Explore chapters and articles related to this topic
Implementation
Published in Seyedeh Leili Mirtaheri, Reza Shahbazian, Machine Learning Theory to Applications, 2022
Seyedeh Leili Mirtaheri, Reza Shahbazian
Apache MXNet is a Deep Learning framework developed with the goal of efficiency and flexibility [118]. Developed by Pedro Domingos and a team of researchers at the University of Washington and a part of the DMLC. It allows the combination of symbolic and imperative programming to enhance efficiency and productivity. MXNet Contains a dynamic dependency scheduler at its core which automatically parallelizes both symbolic and imperative operations impulsively. A graph optimization layer over the scheduler makes symbolic execution fast and resourceful. MXNet is portable and lightweight, scaling adequately to multiple GPUs and machines. It also supports an efficient deployment of trained models in substandard devices for inference, namely mobile devices (using Amalgamation), IoT devices (using AWS Greengrass), Serverless (using AWS Lambda), or containers.
Artificial Intelligence Software and Hardware Platforms
Published in Mazin Gilbert, Artificial Intelligence for Autonomous Networks, 2018
Rajesh Gadiyar, Tong Zhang, Ananth Sankaranarayanan
MXNet (https://mxnet.incubator.apache.org/) is a deep learning framework developed by collaborators from various companies and universities including the likes of Microsoft, Nvidia, Baidu, Intel, Carnegie Mellon University, University of Alberta, and University of Washington, which is currently an Apache incubator project. MXNet supports multiple programming languages, including R, Python, Julia, and Scala.
Medical Internet of things using machine learning algorithms for lung cancer detection
Published in Journal of Management Analytics, 2020
Kanchan Pradhan, Priyanka Chawla
Table 2 explains the comparison among various deep learning frameworks with reference to framework, License, programming language, software support, release date, and supporting algorithms such as CNN and RNN and DBN. In Table 2, it is observed that to develop any software using deep learning C++ and python programming language are mostly used. In Guo et al. (2020), Python was used as programming language with the software support Python3.3 or Jupyter Notebook. It was released in 2017 with the support of CNN, RNN, and DBN. Programming language C++ has been used in frameworks such as PyTorch (Ketkar, 2017), Keras (Jakhar & Hooda, 2018), Caffe (Jia et al., 2014), MXNet (Chen et al., 2015), and TensorFlow (Abadi et al. 2016) to increase speed. Likewise, conveyed estimation gets regular in some recently discharged structures, for example, TensorFlow, MXNet, Keras, and Chainer (Tokui et al., 2019). The objective is to additionally improve the figuring proficiency for deep learning. MXNet underpins a few interfaces including C++, Python, R, Scala, Perl, MATLAB, Javascript,Go (Skoymind, 2017). It bolsters both calculation diagram affirmations and basic calculation presentations for engineering plan. MXNet bolsters information and model parallelism as well as follows parameter server plans to help circulated count too. MXNet is most useful, yet the exhibition isn’t streamlined as much as other condition of the art structures.
Deep Learning Techniques for OFDM Systems
Published in IETE Journal of Research, 2021
M. Meenalakshmi, Saurabh Chaturvedi, Vivek K. Dwivedi
MXNet is an efficient open-source library used for DL applications. It has a hybrid frontend to provide both flexibility and speed. It supports eight-language interfacing, including Julia, Scala, Java, Clojure, R, Python, C++, and Perl [43]. The computation graph declarations and imperative computation declarations are used for the architecture design. MXNet supports data parallelism, model parallelism, and distributed calculation.