Building Recommendation System Using MXNET on AWS Sagemaker

提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

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上級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

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

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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.

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

よくある質問

よくある質問

さらに質問がある場合は、受講者ヘルプセンターにアクセスしてください。