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學生對 Coursera Project Network 提供的 Building Recommendation System Using MXNET on AWS Sagemaker 的評價和反饋


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....

1 - Building Recommendation System Using MXNET on AWS Sagemaker 的 4 個評論(共 4 個)

創建者 Gokulakannan S


Loved the project.

創建者 Kelly P


It was difficult to understand the speaker most of the time. The details of what was being implemented were skipped.

創建者 farruhshahidi1


It is ok not to go through theory as it was mentioned in the beginning of the project. But the instructor just goes through the code and either does not explain or explains poorly. Very low quality project.

創建者 Hui R


I​t's pretty bad. Do not use it.