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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/aws-computer-vision-gluoncv
课程评论：没有评论
课程名称：AWS计算机视觉：使用GluonCV入门 课程概述： 本课程提供计算机视觉（CV）和机器学习（ML）的概述，特别是在亚马逊网络服务（AWS）上构建和训练计算机视觉模型的方法。课程将讨论人工神经网络及其他深度学习概念，并逐步介绍如何将神经网络构建模块组合成完整的计算机视觉模型，并高效地进行训练。 课程涵盖的AWS服务和框架包括：Amazon Rekognition、Amazon SageMaker、Amazon SageMaker GroundTruth、Amazon SageMaker Neo、通过Amazon EC2的AWS深度学习AMI、AWS深度学习容器，以及在AWS上运行的Apache MXNet。课程由视频讲座、动手练习指南、演示和小测验组成。 每周将聚焦计算机视觉及GluonCV的不同方面。第一周将介绍计算机视觉的一些基础概念，讨论GluonCV能解决哪些任务，以及Apache MXNet的优势。 第二周，将重点介绍最适合您任务的AWS服务，包括Amazon Rekognition和Amazon SageMaker。我们将回顾AWS深度学习AMI与深度学习容器之间的区别，并演示如何设置本模块涵盖的每项服务。 第三周将重点设置GluonCV和MXNet。我们将使用预训练模型进行分类、检测和分割任务。 在第四和第五周，我们将深入学习Gluon，这是MXNet的易用高层API，包括理解何时使用不同的Gluon模块、如何将这些模块组合成完整模型、构建数据集以及编写完整的训练循环。 最后一周将包括一个最终项目，您将应用在课程中学习的所有知识：选择适合的预训练GluonCV模型，应用该模型于您的数据集，并可视化GluonCV模型的输出。 课程大纲： 第1部分：模块1 - 计算机视觉简介 第2部分：模块2 - AWS上的机器学习 第3部分：模块3 - 使用GluonCV模型 第4部分：模块4 - Gluon基础 第5部分：模块5 - Gluon基础续 第6部分：模块6 - 最终项目
Part: 1
Title:Module 1: Introduction to Computer Vision
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Part: 2
Title:Module 2: Machine Learning on AWS
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Part: 3
Title:Module 3: Using GluonCV Models
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Part: 4
Title:Module 4: Gluon Fundamentals
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Part: 5
Title:Module 5: Gluon Fundamentals Continued
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Part: 6
Title:Module 6: Final Project
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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.
