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Отзывы учащихся о курсе Using TensorFlow with Amazon Sagemaker от партнера Coursera Project Network

4.7
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Оценки: 84
Рецензии: 9

О курсе

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, 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 learn how to train and deploy an image classifier created and trained with the TensorFlow framework within the Amazon Sagemaker ecosystem. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. However, it is possible to use Sagemaker for custom training scripts as well. We will use TensorFlow and Sagemaker's TensorFlow Estimator to create, train and deploy a model that will be able to classify images of dogs and cats from the popular Oxford IIIT Pet Dataset. Since this is a practical, project-based course, we will not dive in the theory behind deep learning based image classification, but will focus purely on training and deploying a model with Sagemaker and TensorFlow. You will also need to have some experience with Amazon Web Services (AWS). 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....

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1–9 из 9 отзывов о курсе Using TensorFlow with Amazon Sagemaker

автор: Sebastian R G

24 июля 2020 г.

This course will serve as a guide on how to use AWS SageMaker. However, there is no technical challenge to it. In my opinion, since the course is marked as difficult the students should be capable of solving some problems on their own.

I would still encourage people to learn about this tool since it can be used to take models into production with a simple API.

автор: Prafull S

24 июня 2020 г.

The overall project is good.

But May be the coding style should have been better.

For example train.py could have been written in pycharm or even jupyter directly instead of %%writefile.

автор: Metin A

28 дек. 2020 г.

Writing codes line by line was not a good hands-on experience.

But anyways, it was a quick code-based example for TensorFlow on AWS with SageMaker.

автор: Andy H

8 апр. 2020 г.

By far the worst Coursera course I've ever done. The interface is barely useable (two windows in one tab), you have to sign up to various accounts including with a credit card for AWS, and the session times out in just over an hour for the two hour course. I want a refund! Avoid.

автор: Mustafa S

15 февр. 2021 г.

The instructor is great, he tries his best to explain all steps. However, a short project is not enough to grasp all materials in AWS Sagemaker. You may also need general AWS knowledge. There are details that you need to dig out further for better understanding.

автор: Suhaimi W C

27 сент. 2020 г.

Great project and awesome customization. I got to learn a lot and practice what I learned in this class. Thanks to Amit for teaching this class.

автор: purnachand k

16 мая 2020 г.

Very Interesting

автор: tale p

27 июня 2020 г.

good

автор: Brad A

20 дек. 2020 г.

I was glad to find that Tensorflow and Sagemaker were available on Coursera. When working through this project I ran into a couple of snags that weren't addressed in the content and had to go digging in the discussion forums. The first was the Resources limits on AWS regarding the instance type which I needed AWS support to engage on. This created some lag time to finishing the material. The way the course is designed now it assumes that you work through it in one session. Coming back to it creates some issues (such as the model needed to be retrained to be served). This serving portion also didn't work for me out of the box and I'm still trying to find a suitable solution on Inference Endpoints in SageMaker from saved model artifacts on S3.