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Learner Reviews & Feedback for AWS Computer Vision: Getting Started with GluonCV by Amazon Web Services

382 ratings
108 reviews

About the Course

This course provides an overview of Computer Vision (CV), Machine Learning (ML) with Amazon Web Services (AWS), and how to build and train a CV model using the Apache MXNet and GluonCV toolkit. The course discusses artificial neural networks and other deep learning concepts, then walks through how to combine neural network building blocks into complete computer vision models and train them efficiently. This course covers AWS services and frameworks including Amazon Rekognition, Amazon SageMaker, Amazon SageMaker GroundTruth, and Amazon SageMaker Neo, AWS Deep Learning AMIs via Amazon EC2, AWS Deep Learning Containers, and Apache MXNet on AWS. The course is comprised of video lectures, hands-on exercise guides, demonstrations, and quizzes. Each week will focus on different aspects of computer vision with GluonCV. In week one, we will present some basic concepts in computer vision, discuss what tasks can be solved with GluonCV and go over the benefits of Apache MXNet. In the second week, we will focus on the AWS services most appropriate to your task. We will use services such as Amazon Rekognition and Amazon SageMaker. We’ll review the differences between AWS Deep Learning AMIs and Deep Learning containers. Finally, there are demonstrations on how to set up each of the services covered in this module. Week three will focus on setting up GluonCV and MXNet. We will look at using pre-trained models for classification, detection and segmentation. During week four and five, we will go over the fundamentals of Gluon, the easy-to-use high-level API for MXNet: understanding when to use different Gluon blocks, how to combine those blocks into complete models, constructing datasets, and writing a complete training loop. In the final week, there will be a final project where you will apply everything you’ve learned in the course so far: select the appropriate pre-trained GluonCV model, apply that model to your dataset and visualize the output of your GluonCV model....

Top reviews

Mar 17, 2020

I really liked this class. The labs were fun to do. I am hoping to pass the AWS Machine Learning certification and I am hoping this class got me closer to that goal.

May 1, 2021

Comprehensive & Informative. I took nearly 12 weeks to complete the course. Worth the time. Highly recommended for anyone looking to get started with GluonCV.

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101 - 108 of 108 Reviews for AWS Computer Vision: Getting Started with GluonCV

By Sandeep S

Jul 19, 2020


By Kristoffer H

May 21, 2020

If you want a course with a lot of holes between what is covered and what your expected to do then this is the course for you. If you ask for help about stuff not covered in the course it can take a week or more(some people go 15+ days without a response) to get something about it being against the honor code to help. Well its apparently against the honor code to teach too.

By Nikhil Y

May 18, 2020

Not so good course.AWS apply charges on many things.I was worried at every step .And alsos the instructor don't tell clearly how to install dependencies and how to perform task.

By Bharath A

Sep 19, 2020


By oleg r

Feb 20, 2020

It seems this company is not honest. They set deadline but don't react on request about problem in company side. It makes me nervous.

When I run script of Lesson 3 Practice Assignment in my AWS no one error occured

but when I uploaded script here it could not download dataset:

Downloading /home/jovyan/.mxnet/datasets/cifar10/cifar-10-binary.tar.gz from

download failed due to ConnectionError(MaxRetryError("HTTPSConnectionPool(host='', port=443): Max retries exceeded with url: /gluon/dataset/cifar10/cifar-10-binary.tar.gz (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7fa78f41c400>: Failed to establish a new connection: [Errno 110] Connection timed out'))")), retrying, 4 attempts left


Jun 11, 2020

Great learning or platform

By Deleted A

May 16, 2020

Is there a way to cancel?

By Romil N

Jun 14, 2020

marketing stunt