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.
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Über diesen Kurs
We recommend a solid foundation in one of the fields adjacent to the topic of the course, such as Computer Science, Machine Learning, Medicine.
Kompetenzen, die Sie erwerben
- Graphs
- Unsupervised Learning
- Autoencoder
- Deep Learning
We recommend a solid foundation in one of the fields adjacent to the topic of the course, such as Computer Science, Machine Learning, Medicine.
von

University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Lehrplan - Was Sie in diesem Kurs lernen werden
Week 1 - Embedding
An overview of the course and everything about Embedding.
Week 2 - Convolutional Neural Networks (CNN)
We discuss the importance of Convolution and Pooling, and then present relevant information about Convolutional Neural Networks.
Week 3 - Recurrent Neural Networks (RNN)
Recurrent Neural Network have important building blocks. We'll explain those and give examples for healthcare applications.
Week 4 - Autoencoders
Learn why Autoencoders are indispensible in Machine Learning. We'll also show you how this is applied in healthcare.
Über den Spezialisierung Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.

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