O'Reilly、Cloudera 主办
Make Data Work
2017年7月12-13日:培训
2017年7月13-15日:会议
北京,中国

AWS上使用MXNet进行分布式深度学习 (Distributed deep learning on AWS using MXNet)

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

Damon Deng (AWS)
13:30–17:00 Thursday, 2017-07-13
AI应用 (AI applications)
地点: 多功能厅5C(Function Room 5C) 观众水平 (Level): Intermediate

必要预备知识 (Prerequisite Knowledge)

Familiarity with Python, the Jupyter Notebook, machine learning, and data science

需要提前准备的资料和下载 (Materials or downloads needed in advance)

笔记本电脑(最好是macOS但不是必须如果使用AWS),安装好最新稳定版本的Apache MXNet,支持Python,GitHub和AWS的账号。
A laptop (macOS preferred but not required if using AWS) with the latest stable build of Apache MXNet with Python support installed and a GitHub account and an AWS account

您将学到什么 (What you'll learn)

Gain a foundational understanding of deep learning and learn how to set up and install MXNet, including on AWS GPU clusters, and use it for data ingestion, model training, use of multiple GPUs, model fine tuning, deployment in AWS Lambda, AWS Elastic Container Service, and AWS Green Grass

描述 (Description)

深度学习正持续地在诸如计算机视觉、自然语言处理和推荐引擎等领域引领最前沿的进步。带来这个进步的一个关键因素就是大量的高度灵活和对开发人员很友好的深度学习框架的出现。在本辅导课里,亚马逊机器学习团队的成员将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。辅导课的最后,你可以获得上手的机会来获得针对多种应用的经验,包括计算机视觉和推荐引擎等。并可以看到如何使用预先配置好的深度学习AMI和CloudFormation模版来帮助加快开发速度。

模块1. 深度学习的背景知识

模块2. 演示在AWS上如何设置AMI、CloudFormation模版和其他深度学习框架

模块3. 揭开MXNet的面纱看看(MXNet内部)

模块4. 上手使用MXNet:NDArrays、Symbols和训练深度学习神经网络的机制。

模块5. 上手MXNet:针对计算机视觉和推荐引擎的应用例子


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 offers an introduction to the powerful and scalable deep learning framework Apache MXNet. You’ll gain hands-on experience using Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development and leave able to quickly spin up AWS GPU clusters to train at record speeds.

Outline:

  • Background on deep learning
  • A walk-through on setting up AMIs, CloudFormation Templates and other deep learning frameworks on AWS
  • A peek under the Apache MXNet hood (Apache MXNet internals) and a comparison with other deep learning frameworks
  • Hands on with Apache MXNet: NDArrays, symbols, and the mechanics of training deep neural networks
  • Hands on with Apache MXNet: Application examples targeting computer vision and recommendation engines
Photo of Damon Deng

Damon Deng

AWS

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。

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