Building Recommendation System Using MXNET on AWS Sagemaker

提供方
Coursera Project Network
在此指导项目中,您将:

Learn how to train a Recommendation System using Matrix Factorization using AWS Sagemaker.

Deploy it in production on the cloud using AWS Sagemaker.

Clock2 to 3 hours
Advanced高级设置
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project for training the model, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will how to train and deploy a Recommendation System using AWS Sagemaker. We will go through the detailed step by step process of training a recommendation system on the Amazon's Electronics dataset. We will be using a Notebook Instance to build our training model. You will learn how to use Apache's MXNET Deep Learning Model on the AWS Sagemaker platform. Since this is a practical, project-based course, we will not dive in the theory behind recommendation systems, but will focus purely on training and deploying a model with AWS Sagemaker. You will also need to have some experience with Amazon Web Services (AWS) and knowledge of how deep learning frameworks work. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

  • Deep Learning
  • aws
  • sagemaker
  • Python Programming
  • Recommender Systems

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction

  2. Create a AWS Sagemaker Notebook Instance.

  3. Download the data.

  4. Explore and Visualize the data.

  5. Prepare the data.

  6. Building the Network.

  7. Creating the Training Function.

  8. Creating the Deployment Functions.

  9. Training and Deploying the Model.

  10. Evaluating the Model.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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