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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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.

Course 2 of 3 in the Deep Learning for Healthcare Specialization.

Syllabus

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.

Deep Learning for Healthcare Specialization

Deep Learning

Healthcare

Embeddings

Convolutional Neural Networks

CNNs

Recurrent Neural Networks