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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai-deep-learning-capstone
课程评论：没有评论
课程名称：深度学习 AI 毕业设计项目 课程概述：在本课题中，学习者将运用其深度学习知识和技能来解决实际问题。学习者将使用自己选择的库开发和测试深度学习模型，加载和预处理真实问题的数据，建立模型并对其进行验证。最后，学习者将展示项目报告，以证明其模型的有效性和在深度学习领域的专业水平。 学习成果： - 确定在不同情况下使用何种深度学习方法 - 知道如何构建一个深度学习模型以解决实际问题 - 掌握创建深度学习流程的过程 - 将深度学习知识应用于利用真实数据改进模型 - 展示呈现和沟通深度学习项目成果的能力 教学大纲： 1. 模块 1 - 数据加载：介绍本课程将要解决的问题，学习如何加载图像数据集、操作图像及可视化。 2. 模块 2 - 数据处理：学习如何处理图像数据并准备构建分类器，使用预训练模型。 3. 模块 3 - 构建分类器：在 PyTorch 部分，学习如何构建线性分类器；在 Keras 部分，学习如何使用 ResNet50 预训练模型构建图像分类器。 4. 模块 4 - 同伴评审：在 PyTorch 部分，完成同伴评审评估，使用 ResNet18 预训练模型构建图像分类器；在 Keras 部分，同样进行评估，使用 VGG16 预训练模型构建图像分类器，并与前一模块中构建的 ResNet50 模型进行性能比较。
Part: 1
Title:Module 1 - Loading Data
Description:In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
Part: 2
Title:Module 2
Description:In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
Part: 3
Title:Module 3
Description:In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.
Part: 4
Title:Module 4
Description:In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning. Learning Outcomes: • determine what kind of deep learning method to use in which situation • know how to build a deep learning model to solve a real problem • master the process of creating a deep learning pipeline • apply knowledge of deep learning to improve models using real data • demonstrate ability to present and communicate outcomes of deep learning projects
