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AWS Computer Vision: Getting Started with GluonCV

Approx. 31 hours to complete

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Course Summary

This course teaches you how to build computer vision applications using Amazon Web Services and GluonCV. You will learn how to train models, deploy them on AWS, and integrate them with other applications.

Key Learning Points

Build computer vision applications using Amazon Web Services and GluonCV
Train models and deploy them on AWS
Integrate computer vision applications with other applications

Learning Outcomes

Build computer vision applications using AWS and GluonCV
Train models and deploy them on AWS
Integrate computer vision applications with other applications

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Related Education Paths

Notable People in This Field

Andrew Ng
Fei-Fei Li

Related Books

Description

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.

Outline

Module 1: Introduction to Computer Vision
Meet the Instructors
Weekly Overview
Computer Vision Overview
Computer Vision Tasks
GluonCV
GluonCV Model Zoo
Apache MXNet
Imperative vs Symbolic
Weekly Summary
Pre-Course Survey
Mid-Course Survey
Module 1 Quiz

Module 2: Machine Learning on AWS
Weekly Overview
AWS Machine Learning Stack
Amazon Rekognition
Amazon Rekognition: Demonstration
Amazon SageMaker
Amazon SageMaker: Demonstration
AWS Deep Learning AMIs
AWS Deep Learning AMIs: Demonstration
AWS Deep Learning Containers
AWS Deep Learning Containers: Demonstration
Weekly Summary
Module 2 Quiz

Module 3: Using GluonCV Models
Weekly Overview
Setting up GluonCV
Image Classification
Image Classification: One Line Demonstration
Image Classification: Step by Step Demonstration
Object Detection
Object Detection: Step by Step Demonstration
Image Segmentation
Image Segmentation: Step by Step Demonstration
Neural Network Essentials: Fully Connected
Neural Network Essentials: Convolution and Max Pooling
Weekly Summary

Module 4: Gluon Fundamentals
Weekly Overview
MXNet NDArrays
MXNet vs NumPy NDArray
Common NDArray Operations
Gluon Blocks
Initialization of Gluon Blocks
Sequential Gluon Blocks
Custom Gluon Blocks
Visualization of Gluon Blocks
Metrics vs Losses
Metrics
Losses
Weekly Summary

Module 5: Gluon Fundamentals Continued
Weekly Overview
Automatic Differentiation
MXNet AutoGrad
MXNet Optimizers
Gluon Trainers
Data in Machine Learning
Gluon Datasets
Gluon Transformations
Gluon DataLoaders
Neural Network Training
Neural Network Evaluation
Weekly Summary
Lesson 1 Quiz
Lesson 2 Quiz

Module 6: Final Project
Counting People in Images
Course Summary
Post-Course Survey

Summary of User Reviews

Learn about AWS Computer Vision with GluonCV on Coursera. Read reviews to see what users thought about this course.

Key Aspect Users Liked About This Course

The course provides a comprehensive overview of AWS computer vision with GluonCV, making it easy for beginners to grasp the concepts.

Pros from User Reviews

Clear and concise explanations of complex concepts
Well-structured course with practical examples
Great resources and support from instructors
Hands-on exercises and quizzes to reinforce learning

Cons from User Reviews

Some users found the pace of the course too slow
The assignments were challenging for some users
Some users felt that the course could have provided more real-world examples
The course may not be suitable for advanced users who are already familiar with AWS computer vision

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