Presented by O'Reilly and Cloudera
Make Data Work
July 12-13, 2017: Training
July 13-15, 2017: Tutorials & Conference
Beijing, China

AWS上的MXNet (MXNet on AWS)

此演讲使用中文 (This will be presented in Chinese)

Damon Deng (AWS)
13:10–13:50 Friday, 2017-07-14
AI应用 (AI applications)
Location: 报告厅(Auditorium) 观众水平 (Level): Intermediate
平均得分:: ****.
(4.00, 1 次得分)

必要预备知识 (Prerequisite Knowledge)

Experience with machine learning

您将学到什么 (What you'll learn)

Gain foundational deep learning knowledge and learn how to set up and use MXNet, including on AWS GPU clusters, for data ingestion and model training

描述 (Description)


Damon Deng会就深度学习的背景做一个简短的介绍,主要关注与其相关的应用领域。并会对强大和可扩展的深度学习框架——MXNet——做一个介绍。加入本议题来学习MXNet如何工作以及如何能够快速地利用AWS GPU集群来以世界纪录速度进行训练。

Topics include:

  • 深度学习应用的综述

  • MXNet的设计原则
  • MXNet的基本概念
  • AWS GPU集群和AMI接口

  • MXNet的演示

Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks.

Damon Deng provides a short background on deep learning, focusing on relevant application domains, and offers an introduction to using the powerful and scalable deep learning framework MXNet. Join in to learn how MXNet works and how you can spin up AWS GPU clusters to train at record speeds.

Topics include:

  • A general introduction to deep learning applications
  • MXNet design principles
  • MXNet basic concepts
  • AWS GPU clusters and AMIs
  • A MXNet demonstration
Photo of Damon Deng

Damon Deng


AWS解决方案架构师;拥有17年IT 领域的工作经验,先后在IBM,RIM,Apple 等企业担任工程师、架构师等职位;目前就职于AWS,担任解决方案架构师一职。喜欢编程,喜欢各种编程语言,尤其喜欢Lisp。喜欢新技术,喜欢各种技术挑战,目前在集中精力学习分布式计算环境下的机器学习算法以及深度神经网络框架。

Connect with O'ReillyData

Use the QR Code to follow OReillyData and get the latest conference information and browse data articles.

WeChat QRcode


Stay Connected Image 1
Stay Connected Image 3
Stay Connected Image 2

Read the latest ideas on big data.

ORB Data Site