AWS Computer Vision: Getting Started with GluonCV

开始时间: 04/04/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/aws-computer-vision-gluoncv

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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.

AWS计算机视觉:GluonCV入门:本课程概述了计算机视觉(CV),使用Amazon Web Services(AWS)的机器学习(ML)以及如何使用Apache MXNet和GluonCV工具包构建和训练CV模型。该课程讨论了人工神经网络和其他深度学习概念,然后逐步介绍了如何将神经网络构建模块组合成完整的计算机视觉模型并对其进行有效的训练。   本课程涵盖AWS服务和框架,包括Amazon Rekognition,Amazon SageMaker,Amazon SageMaker GroundTruth和Amazon SageMaker Neo,通过Amazon EC2的AWS Deep Learning AMI,AWS Deep Learning Containers和AWS上的Apache MXNet。该课程包括视频讲座,动手练习指南,演示和测验。   每周将重点关注GluonCV的计算机视觉的不同方面。在第一周,我们将介绍计算机视觉的一些基本概念,讨论使用GluonCV可以解决哪些任务,并介绍Apache MXNet的好处。   在第二周,我们将重点介绍最适合您任务的AWS服务。我们将使用诸如Amazon Rekognition和Amazon SageMaker之类的服务。我们将回顾AWS深度学习AMI与深度学习容器之间的区别。最后,有关于如何设置此模块中涵盖的每个服务的演示。   第三周将重点设置GluonCV和MXNet。我们将研究使用经过预训练的模型进行分类,检测和细分。   在第四和第五周,我们将介绍Gluon的基础知识,Gluon是MXNet的易于使用的高级API:了解何时使用不同的Gluon块,如何将这些块组合成完整的模型,构建数据集并编写完整的培训循环。   在最后一周,将有一个最终项目,您将应用迄今为止在该课程中学到的一切:选择适当的经过预先训练的GluonCV模型,将该模型应用于您的数据集,并可视化GluonCV模型的输出。

课程大纲

Module 1: Introduction to Computer Vision
Module 2: Machine Learning on AWS
Module 3: Using GluonCV Models
Module 4: Gluon Fundamentals
Module 5: Gluon Fundamentals Continued
Module 6: Final Project

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