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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-deep-learning-methods-healthcare
课程评论：没有评论
课程名称：医疗保健的高级深度学习方法 概述：本课程涵盖深度学习（DL）方法、医疗数据及其在医疗应用中的使用。课程包括视频讲座、自主编程实验、作业（包括书面和编程任务）以及一个大型项目。课程的第一阶段将包括关于不同深度学习和健康应用主题的视频讲座、自主实验和多项作业。在这一阶段，学员将积累在医疗数据上开发实用深度学习模型的知识和经验。课程的第二阶段将专注于一个大型项目，学员将研发深度学习模型以解决特定的医疗问题，并形成技术报告和功能演示。我们希望最优秀的项目有可能成为科学出版物。 课程大纲： 第1周 - 注意力模型：介绍注意力模型如何用于检测数据源中的特定特征，以及其在心脏衰竭风险评估中的应用。 第2周 - 图神经网络：讲解图神经网络的基本原理。 第3周 - 记忆网络：介绍记忆网络的原理及其在医疗应用中的预测用法。 第4周 - 生成模型：讨论生成网络及变分自编码器的方法。 该课程致力于帮助学员掌握先进的深度学习技术，推动医疗健康领域的创新与发展。
Part: 1
Title:Week 1 - Attention Models
Description:Attention Models are useful to detect specific features in a data source. We'll explain how it can be applied to the risk of heart failure.
Part: 2
Title:Week 2 - Graph Neural Networks
Description:In this week we'll explain the fundamentals of Graph Neural Networks.
Part: 3
Title:Week 3 - Memory Networks
Description:We'll explain the principles behind Memory Networks and how they can be used for predictions in medical applications.
Part: 4
Title:Week 4 - Generative Models
Description:We'll discuss Generative Networks, as well as the method of Variational Autoencoder
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
